diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..e3515c5 --- /dev/null +++ b/.flake8 @@ -0,0 +1,6 @@ +[flake8] +max-line-length = 100 +ignore = + E203, + W503, + E402 diff --git a/.gitallowed b/.gitallowed new file mode 100644 index 0000000..3f953f2 --- /dev/null +++ b/.gitallowed @@ -0,0 +1 @@ +git secrets --add 'PRIVATE KEY' diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..260d5a7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,5 @@ +# Ignore Jupyter notebooks for language statistics +*.ipynb linguist-documentation=true + +# Set the primary language to Python +*.py linguist-language=Python diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index bd3091b..e416c4d 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -47,8 +47,8 @@ jobs: - name: Set up Poetry virtual environment run: | poetry config virtualenvs.in-project true - poetry install --no-root + poetry install --with dev,test,docs - name: Run Makefile tasks run: | - make all + poetry run make all diff --git a/.gitignore b/.gitignore index ac439d1..86f1d15 100644 --- a/.gitignore +++ b/.gitignore @@ -86,3 +86,6 @@ ipython_config.py *.npy *.npz *.pkl + +# Other +.DS_Store diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index a77157a..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,24 +0,0 @@ -image: python:3.11 - -stages: - - test - - docs - -before_script: - - python -V - - pip install . - -test: - stage: test - script: - - pip install pytest - - pytest tests/ - rules: - - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH - -docs: - script: - - pip install sphinx - - make html - rules: - - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 31a5fe2..0000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,12 +0,0 @@ -repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v2.3.0 - hooks: - - id: check-yaml - - id: end-of-file-fixer - - id: trailing-whitespace - - id: detect-private-key -- repo: https://github.com/psf/black - rev: 22.10.0 - hooks: - - id: black diff --git a/CHANGELOG.md b/CHANGELOG.md index 9574dee..1da5184 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,18 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.8.0] - 2024-07-20 + +### Added + +- Add Linear2DBasisQFE + +### Chore + +- Move type aliases to a separate module +- Update Makefile +- Add github workflows + ## [0.7.0] - 2024-02-06 ### Added diff --git a/Makefile b/Makefile index b97de95..4597dc5 100644 --- a/Makefile +++ b/Makefile @@ -1,20 +1,40 @@ -# Minimal makefile for Sphinx documentation +# Minimal makefile for Sphinx documentation and project maintenance tasks # -# You can set these variables from the command line, and also -# from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = docs BUILDDIR = build -# Put it first so that "make" without argument is like "make help". +default: all + +all: docs-html format format_check static test_coverage secrets_check + help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile +docs-%: + @$(SPHINXBUILD) -M $* "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +format: + black --line-length 100 qulearn tests + isort --multi-line 3 --trailing-comma --force-grid-wrap 0 --use-parentheses --line-width 100 qulearn tests + +format_check: + black --line-length 100 --check qulearn tests + isort --multi-line 3 --trailing-comma --force-grid-wrap 0 --use-parentheses --line-width 100 qulearn tests --check-only + +static: + flake8 qulearn tests + mypy qulearn tests --ignore-missing-imports --no-strict-optional + +test: + pytest tests/ + +test_coverage: + coverage run --source=qulearn --module pytest -v tests/ && coverage report -m + +secrets_check: + @git secrets --scan -r -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +.PHONY: help docs-% format format_check static test test_coverage secrets_check diff --git a/README.md b/README.md index b673707..607f242 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # QuLearn -Welcome to QuLearn, a Python package designed to simplify the development and application of quantum and classical machine learning models. It includes a collection of QML applications from Fraunhofer ITWM. +Welcome to QuLearn, a Python package designed to simplify the development and application of quantum and classical machine learning models. +This project remained a hobby and is not actively developed anymore. +It is very difficult to get people to build something they don't have to... ## About @@ -8,13 +10,12 @@ QuLearn is built on top of [PyTorch](https://pytorch.org/) and [PennyLane](https QuLearn is suitable for various research applications and aims to democratize access to the exciting field of quantum machine learning. It serves as a platform for researchers, developers, and enthusiasts to implement, experiment, and contribute to this rapidly evolving field. -QuLearn also houses QML applications from Fraunhofer ITWM. - ## Getting Started ### Installation -(Installation instructions will be added soon.) +Package is not available on PyPI. +It can be installed from source via pip, works most of the time. ### Basic Usage diff --git a/ToDo.md b/ToDo.md new file mode 100644 index 0000000..9eca479 --- /dev/null +++ b/ToDo.md @@ -0,0 +1,6 @@ +- add input checking +- qlayer: remove item() calls for compatibility +- qlayer: handle 2D case where x or y are out of bounds +- qlayer: too much boiler plate code, reuse mps module +- pennylane non-default devices with lightning qubits, adjoint differentation and Hamiltonians +are bugged, the observable weights are not differentiated \ No newline at end of file diff --git a/ToDoNotes.txt b/ToDoNotes.txt deleted file mode 100644 index e69de29..0000000 diff --git a/docs/conf.py b/docs/conf.py index 18939eb..b7f5b02 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -9,7 +9,7 @@ project = "QuLearn" copyright = "2023, Mazen Ali" author = "Mazen Ali" -release = "0.1.0" +release = "0.8.0" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -24,4 +24,4 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "alabaster" -html_static_path = ["_static"] +html_static_path = [] diff --git a/poetry.lock b/poetry.lock index 2bc3587..5b39946 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,47 +1,48 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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+++ b/qulearn/__init__.py @@ -1,9 +1,12 @@ -import toml import os +import toml + + def get_version(): here = os.path.dirname(os.path.realpath(__file__)) version_info = toml.load(os.path.join(here, "../pyproject.toml")) - return version_info['tool']['poetry']['version'] + return version_info["tool"]["poetry"]["version"] + -__version__ = get_version() \ No newline at end of file +__version__ = get_version() diff --git a/qulearn/datagen.py b/qulearn/datagen.py index 2bc6160..52183fc 100644 --- a/qulearn/datagen.py +++ b/qulearn/datagen.py @@ -1,28 +1,24 @@ -from typing import Optional, TypeVar, Generic, Tuple, Dict, Set, List - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - from abc import ABC, abstractmethod -import torch -from torch.nn import Module -from torch.utils.data import TensorDataset, DataLoader -import numpy as np from itertools import product +from typing import Generic, Optional, Set, Tuple, TypeVar + +import numpy as np +import torch from scipy.stats import qmc -Tensor: TypeAlias = torch.Tensor -Array: TypeAlias = np.ndarray -Device: TypeAlias = torch.device -DataOut: TypeAlias = Dict[str, Tensor] -Loader: TypeAlias = DataLoader -Model: TypeAlias = Module -ParameterList: TypeAlias = List[List[Tensor]] -CDevice: TypeAlias = torch.device -DType: TypeAlias = torch.dtype +from .types import ( + Array, + CDevice, + DataLoader, + DataOut, + Device, + DType, + Model, + ParameterList, + Tensor, + TensorDataset, +) + D = TypeVar("D") L = TypeVar("L") @@ -117,7 +113,7 @@ def gen_data(self, *args, **kwargs) -> D: pass -class DataGenCapacity(DataGenTorch[DataOut, Loader]): +class DataGenCapacity(DataGenTorch[DataOut, DataLoader]): """ Generates data for memory capacity estimation. @@ -174,7 +170,7 @@ def gen_data(self, N: int) -> DataOut: return data - def data_to_loader(self, data: DataOut, s: int) -> Loader: + def data_to_loader(self, data: DataOut, s: int) -> DataLoader: """ Convert data to pytorch loader. @@ -182,7 +178,7 @@ def data_to_loader(self, data: DataOut, s: int) -> Loader: :type data: DataOut :param s: Current label sample. :type s: int - :rtype: Loader + :rtype: DataLoader :raises ValueError: For invalid data or index s. """ self._check_data(data) @@ -222,7 +218,7 @@ def _check_data(self, data: DataOut): raise ValueError(f"Y must be 3-dim (not {check})") -class DataGenFat(DataGenTorch[DataOut, Loader]): +class DataGenFat(DataGenTorch[DataOut, DataLoader]): """ Generates data for estimating fat shattering dimension. @@ -274,7 +270,7 @@ def gen_data(self, d: int) -> DataOut: return data - def data_to_loader(self, data: DataOut, sr: int, sb: int) -> Loader: + def data_to_loader(self, data: DataOut, sr: int, sb: int) -> DataLoader: """ Convert data to pytorch loader. @@ -285,7 +281,7 @@ def data_to_loader(self, data: DataOut, sr: int, sb: int) -> Loader: :param sb: Current b sample. :type sb: int :returns: Pytorch data loader. - :rtype: Loader + :rtype: DataLoader :raises ValueError: For invalid data or indeces sr or sb. """ self._check_data(data) @@ -328,7 +324,7 @@ def _check_data(self, data: DataOut): raise ValueError(f"Y must be 4-dim (not {check})") -class DataGenRademacher(DataGenTorch[DataOut, Loader]): +class DataGenRademacher(DataGenTorch[DataOut, DataLoader]): """ Generates uniform data for estimating the empirical Rademacher complexity. @@ -377,16 +373,14 @@ def gen_data(self, m: int) -> DataOut: X = self.prior.gen_data(m * self.num_data_samples) X = torch.reshape(X, (self.num_data_samples, m, self.prior.sizex)) - sigmas = gen_sigmas( - m=m * self.num_sigma_samples, seed=self.seed, device=self.device - ) + sigmas = gen_sigmas(m=m * self.num_sigma_samples, seed=self.seed, device=self.device) sigmas = torch.reshape(sigmas, (self.num_sigma_samples, m)) data = {"X": X, "sigmas": sigmas} return data - def data_to_loader(self, data: DataOut, s: int) -> Loader: + def data_to_loader(self, data: DataOut, s: int) -> DataLoader: """ Convert data to pytorch loader. @@ -395,7 +389,7 @@ def data_to_loader(self, data: DataOut, s: int) -> Loader: :param s: Current sample. :type s: int :return: Pytorch data loader. - :rtype: Loader + :rtype: DataLoader :raises ValueError: For invalid data or index s. """ self._check_data(data) @@ -442,9 +436,7 @@ class UniformPrior(PriorTorch[Tensor]): :param kwargs: Keyword arguments passed to the base class. """ - def __init__( - self, sizex: int, scale: float = 2.0, shift: float = -1.0, **kwargs - ) -> None: + def __init__(self, sizex: int, scale: float = 2.0, shift: float = -1.0, **kwargs) -> None: super().__init__(sizex, **kwargs) self.scale = scale @@ -487,9 +479,7 @@ class NormalPrior(PriorTorch[Tensor]): :param kwargs: Keyword arguments passed to the base class. """ - def __init__( - self, sizex: int, scale: float = 1.0, shift: float = 0.0, **kwargs - ) -> None: + def __init__(self, sizex: int, scale: float = 1.0, shift: float = 0.0, **kwargs) -> None: super().__init__(sizex, **kwargs) self.scale = scale @@ -778,8 +768,7 @@ def gen_synthetic_labels_fat( if d1 != d2: raise ValueError( - f"The length of b[0] and r[0] are {d1} and {d2}. " - f"Should be constant and the same." + f"The length of b[0] and r[0] are {d1} and {d2}. " f"Should be constant and the same." ) labels = np.zeros((Sr, Sb, d, 1)) @@ -796,9 +785,7 @@ def gen_synthetic_labels_fat( return y -def gen_sigmas( - m: int, seed: Optional[int] = None, device: Device = torch.device("cpu") -) -> Tensor: +def gen_sigmas(m: int, seed: Optional[int] = None, device: Device = torch.device("cpu")) -> Tensor: """ Random vector of +-1. @@ -819,11 +806,7 @@ def gen_sigmas( generator = torch.manual_seed(seed_) - sigmas = ( - torch.randint(2, (m,), device=device, requires_grad=False, generator=generator) - * 2 - - 1 - ) + sigmas = torch.randint(2, (m,), device=device, requires_grad=False, generator=generator) * 2 - 1 return sigmas @@ -900,10 +883,7 @@ def generate_model_lhs_samples( ) samples.append(sample_parameter.reshape((n_samples,) + p.shape)) parameter_list = [ - [ - torch.tensor(samples[j][i], device=device, dtype=dtype) - for j in range(len(samples)) - ] + [torch.tensor(samples[j][i], device=device, dtype=dtype) for j in range(len(samples))] for i in range(n_samples) ] diff --git a/qulearn/fat.py b/qulearn/fat.py index 44f7b82..c266e7e 100644 --- a/qulearn/fat.py +++ b/qulearn/fat.py @@ -1,24 +1,14 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import logging -import torch -import pennylane as qml -from .datagen import DataGenFat -from .trainer import SupervisedTrainer +import torch -Tensor: TypeAlias = torch.Tensor -Model: TypeAlias = qml.QNode -Datagen: TypeAlias = DataGenFat -Trainer: TypeAlias = SupervisedTrainer +from .datagen import DataGenFat as Datagen +from .trainer import SupervisedTrainer as Trainer +from .types import QModel, Tensor def fat_shattering_dim( - model: Model, + model: QModel, datagen: Datagen, trainer: Trainer, dmin: int, @@ -30,7 +20,7 @@ def fat_shattering_dim( Estimate the fat-shattering dimension for a model with a given architecture. :param model: The model. - :type model: Model + :type model: QModel :param datagen: The (synthetic) data generator. :type datagen: Datagen :param trainer: The trainer. @@ -64,13 +54,13 @@ def fat_shattering_dim( def check_shattering( - model: Model, datagen: Datagen, trainer: Trainer, d: int, gamma: float + model: QModel, datagen: Datagen, trainer: Trainer, d: int, gamma: float ) -> bool: """ Check if the model shatters a given dimension d with margin value gamma. :param model: The model. - :type model: Model + :type model: QModel :param datagen: The (synthetic) data generator. :type datagen: Datagen :param trainer: The trainer. diff --git a/qulearn/fim.py b/qulearn/fim.py index d71dc6d..52ac422 100644 --- a/qulearn/fim.py +++ b/qulearn/fim.py @@ -1,19 +1,10 @@ -from typing import List, Iterable - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import math from math import pi +from typing import List + import torch -from torch.nn import Module -Tensor: TypeAlias = torch.Tensor -Model: TypeAlias = Module -ParameterList: TypeAlias = List[Iterable[Tensor]] +from .types import Model, ParameterList, Tensor def compute_effdim( @@ -119,9 +110,7 @@ def mc_integrate_fims_effdim( sum += weight * torch.exp(logdet - zeta) sum /= num_samples - result = 2.0 * zeta / torch.log(kappa) + 2.0 / torch.log(kappa) * torch.log( - 1.0 / volume * sum - ) + result = 2.0 * zeta / torch.log(kappa) + 2.0 / torch.log(kappa) * torch.log(1.0 / volume * sum) return result @@ -147,9 +136,7 @@ def half_log_det(fim: Tensor, c: Tensor) -> Tensor: _check_fim(fim) eigs = torch.linalg.eigvalsh(fim) - result = torch.tensor(0.5, device=fim.device, dtype=fim.dtype) * sum( - torch.log(1.0 + c * eigs) - ) + result = torch.tensor(0.5, device=fim.device, dtype=fim.dtype) * sum(torch.log(1.0 + c * eigs)) return result @@ -182,9 +169,7 @@ def const_effdim(num_samples: int, gamma: Tensor) -> Tensor: return const -def norm_const_fim( - trace_integral: Tensor, num_parameters: int, volume: Tensor -) -> Tensor: +def norm_const_fim(trace_integral: Tensor, num_parameters: int, volume: Tensor) -> Tensor: """ Computes the normalization constant for the Fisher Information Matrix (FIM). @@ -322,7 +307,8 @@ def empirical_fim(model: Model, features: Tensor) -> Tensor: :raises ValueError: If invalid features format. .. note:: - This function assumes that the output of the model is a differentiable tensor of probabilities. + This function assumes that the output of the model is a differentiable + tensor of probabilities. """ _check_features(features) diff --git a/qulearn/hat_basis.py b/qulearn/hat_basis.py index 2e2f122..83cdeb9 100644 --- a/qulearn/hat_basis.py +++ b/qulearn/hat_basis.py @@ -1,14 +1,8 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - from typing import Tuple import torch -Tensor: TypeAlias = torch.Tensor +from .types import Tensor class HatBasis: @@ -35,9 +29,9 @@ def position(self, x: Tensor) -> Tensor: """ Find the index of the grid point left of x. - :param x: A tensor containing the values for which the the position indeces are to be found. + :param x: A tensor containing the values for which the position indexes are to be found. :type x: Tensor - :returns: A tensors of position indeces. + :returns: A tensors of position indexes. The position indices are -1 for values left of `a`, and -2 for values right of `b`. :rtype: Tensor """ @@ -47,9 +41,7 @@ def position(self, x: Tensor) -> Tensor: within_range = torch.logical_not(torch.logical_or(left_of_a, right_of_b)) position = torch.zeros_like(x) - position[within_range] = ( - (x[within_range] - self.a) / self.segment_length - ).floor() + position[within_range] = ((x[within_range] - self.a) / self.segment_length).floor() position[left_of_a] = -1 position[right_of_b] = -2 @@ -60,9 +52,11 @@ def grid_points(self, x: Tensor) -> Tuple[Tensor, Tensor]: """ Finds the grid points surrounding given values in the discretized space. - :param x: A tensor containing the values for which the surrounding grid points are to be found. + :param x: A tensor containing the values for which the surrounding + grid points are to be found. :type x: Tensor - :returns: A tuple of two tensors. The first tensor contains the left boundary points of the segments, the second tensor contains the right boundary points of the segments. + :returns: A tuple of two tensors. The first tensor contains the left boundary points + of the segments, the second tensor contains the right boundary points of the segments. :rtype: Tuple[Tensor, Tensor] """ diff --git a/qulearn/loss.py b/qulearn/loss.py index 0a5bef3..6359ae2 100644 --- a/qulearn/loss.py +++ b/qulearn/loss.py @@ -1,15 +1,8 @@ from typing import Optional -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import torch -Tensor: TypeAlias = torch.Tensor -Loss: TypeAlias = torch.nn.Module +from .types import Loss, Tensor class RademacherLoss(Loss): diff --git a/qulearn/memory.py b/qulearn/memory.py index 391ada8..5cbc416 100644 --- a/qulearn/memory.py +++ b/qulearn/memory.py @@ -1,24 +1,11 @@ -from typing import List, Tuple - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import logging -import torch -from torch.nn import Module -import numpy as np -from .trainer import SupervisedTrainer -from .datagen import DataGenCapacity +import numpy as np +import torch -Tensor: TypeAlias = torch.Tensor -Model: TypeAlias = Module -Datagen: TypeAlias = DataGenCapacity -Trainer: TypeAlias = SupervisedTrainer -Capacity = List[Tuple[int, float, int, int]] +from .datagen import DataGenCapacity as Datagen +from .trainer import SupervisedTrainer as Trainer +from .types import Capacity, Model def memory( diff --git a/qulearn/mps.py b/qulearn/mps.py index 397b631..3df4f08 100644 --- a/qulearn/mps.py +++ b/qulearn/mps.py @@ -1,18 +1,12 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - +import math from typing import List -import math -import torch import tntorch +import torch + from qulearn.hat_basis import HatBasis -MPS: TypeAlias = tntorch.tensor.Tensor -Tensor: TypeAlias = torch.Tensor +from .types import MPS, Tensor class MPSQGates: @@ -105,7 +99,8 @@ def contract(self, mps, L: int) -> Tensor: def left_core_reshape(self, core: Tensor) -> Tensor: """ - Reshapes a core tensor for the left-most core, preparing it for SVD and embedding into a unitary matrix. + Reshapes a core tensor for the left-most core, + preparing it for SVD and embedding into a unitary matrix. :param core: The core tensor to reshape. :type core: Tensor @@ -136,14 +131,16 @@ def reg_core_reshape(self, core: Tensor) -> Tensor: class HatBasisMPS: """ - Generates Matrix Product States (MPS) corresponding to evaluations of linear hat basis functions. + Generates Matrix Product States (MPS) corresponding to evaluations + of linear hat basis functions. :param basis: The hat basis to use for generating the MPS. :type basis: HatBasis .. note:: - The number of nodes in the hat basis must be a power of 2, corresponding to the number of qubits used. - Currently works only for scalar inputs x. + The number of nodes in the hat basis must be a power of 2, + corresponding to the number of qubits used. + Currently, works only for scalar inputs x. """ def __init__(self, basis: HatBasis) -> None: @@ -151,9 +148,7 @@ def __init__(self, basis: HatBasis) -> None: num_qubits = math.log2(basis.num_nodes) if not num_qubits.is_integer(): - raise ValueError( - f"Number of nodes ({basis.num_nodes}) " "must be a power of 2." - ) + raise ValueError(f"Number of nodes ({basis.num_nodes}) " "must be a power of 2.") self.num_sites = int(num_qubits) @@ -193,7 +188,7 @@ def eval(self, x: Tensor) -> MPS: def mps_hatbasis(self, first: float, second: float, idx: int) -> MPS: """ - Generates an MPS the hat basis vector. + Generates an MPS for the hat basis vector. :param first: The first non-zero value in the hat basis function. :type first: float diff --git a/qulearn/mps_kronprod.py b/qulearn/mps_kronprod.py new file mode 100644 index 0000000..e7adf29 --- /dev/null +++ b/qulearn/mps_kronprod.py @@ -0,0 +1,99 @@ +import tntorch +import torch + +from .types import MPS + + +def kron(tleft: MPS, tright: MPS) -> MPS: + """ + Performs the Kronecker product of two MPS tensors. + + :param tleft: The first MPS tensor. + :type tleft: MPS + :param tright: The second MPS tensor. + :type tright: MPS + :return: The MPS tensor resulting from the Kronecker product of `tleft` and `tright`. + :rtype: MPS + """ + c1 = tleft.cores + c2 = tright.cores + c3 = c1 + c2 + t3 = tntorch.Tensor(c3) + + return t3 + + +def zkron(tleft: MPS, tright: MPS) -> MPS: + """ + Performs the z-ordered Kronecker product of two MPS tensors. + See https://arxiv.org/abs/1802.02839. + + :param tleft: The first MPS tensor. + :type tleft: MPS + :param tright: The second MPS tensor. + :type tright: MPS + :return: The MPS tensor resulting from the Kronecker product of `tleft` and `tright`. + :rtype: MPS + """ + _core_length_check(tleft, tright) + + coresleft = tleft.cores + coresright = tright.cores + + if len(coresleft) != len(coresright): + raise ValueError("The number of cores in the left and right MPS must be the same.") + + coresout = [] + + for i in range(len(coresleft)): + coreleft = coresleft[i] + coreright = coresright[i] + rankleft1 = coreleft.shape[0] + rankleft2 = coreleft.shape[-1] + rankright1 = coreright.shape[0] + rankright2 = coreright.shape[-1] + + site_dim = coreleft.shape[1] + core = torch.empty((rankleft1 * rankright1, site_dim, rankleft2 * rankright1)) + for k in range(site_dim): + core[:, k, :] = torch.kron(coreleft[:, k, :], torch.eye(rankright1)) + coresout.append(core) + + site_dim = coreright.shape[1] + core = torch.empty((rankleft2 * rankright1, site_dim, rankleft2 * rankright2)) + for k in range(site_dim): + core[:, k, :] = torch.kron(torch.eye(rankleft2), coreright[:, k, :]) + coresout.append(core) + + tout = tntorch.Tensor(coresout) + return tout + + +def zkron_joined(tleft, tright): + """ + Performs the z-ordered Kronecker product of two MPS tensors, + and joins the physical indices of tleft and tright into one. + See https://arxiv.org/abs/1802.02839. + + :param tleft: The first MPS tensor. + :type tleft: MPS + :param tright: The second MPS tensor. + :type tright: MPS + :return: The MPS tensor resulting from the Kronecker product of `tleft` and `tright`. + :rtype: MPS + """ + _core_length_check(tleft, tright) + + c1 = tleft.cores + c2 = tright.cores + c3 = [torch.kron(a, b) for a, b in zip(c1, c2)] + + t3 = tntorch.Tensor(c3) + return t3 + + +def _core_length_check(tleft, tright): + coresleft = tleft.cores + coresright = tright.cores + if len(coresleft) != len(coresright): + raise ValueError("The number of cores in the left and right MPS must be the same.") diff --git a/qulearn/observable.py b/qulearn/observable.py index 0723532..2430e2f 100644 --- a/qulearn/observable.py +++ b/qulearn/observable.py @@ -1,22 +1,11 @@ -from typing import List, Tuple, Sequence - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import math -import torch +from typing import List + import pennylane as qml +from .types import Hamiltonian, Observable, ParitySequence, Tensor from .utils import all_bin_sequences -Tensor: TypeAlias = torch.Tensor -Observable: TypeAlias = qml.operation.Observable -Hamiltonian: TypeAlias = qml.Hamiltonian -ParitySequence: TypeAlias = Sequence[Tuple[int, ...]] - def parity_all_hamiltonian(num_qubits: int, weights: Tensor) -> Hamiltonian: """ diff --git a/qulearn/qkernel.py b/qulearn/qkernel.py index ba4f28c..2065a26 100644 --- a/qulearn/qkernel.py +++ b/qulearn/qkernel.py @@ -1,24 +1,14 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - from typing import Optional import pennylane as qml import torch from torch import nn -from .qlayer import CircuitLayer +from .qlayer import CircuitLayer as FeatureEmbed DEFAULT_QDEV_CFG = {"name": "default.qubit", "shots": None} -FeatureEmbed: TypeAlias = CircuitLayer -Tensor: TypeAlias = torch.Tensor -QDevice: TypeAlias = qml.Device -QNode: TypeAlias = qml.QNode -Expectation: TypeAlias = qml.measurements.ExpectationMP +from .types import Expectation, QDevice, QNode, Tensor class QKernel(nn.Module): diff --git a/qulearn/qlayer.py b/qulearn/qlayer.py index 23a2c18..0d556ef 100644 --- a/qulearn/qlayer.py +++ b/qulearn/qlayer.py @@ -1,37 +1,31 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - -from typing import Iterable, Any, Optional, Union, Dict - -from enum import Enum import math +from enum import Enum +from typing import Dict, Iterable, Optional + +import pennylane as qml import torch from torch import nn -import pennylane as qml from .hat_basis import HatBasis from .mps import HatBasisMPS, MPSQGates +from .mps_kronprod import kron, zkron +from .types import ( + CDevice, + DType, + Entropy, + Expectation, + Observable, + Observables, + Probability, + QDevice, + QNode, + Sample, + Tensor, + Wires, +) DEFAULT_QDEV_CFG = {"name": "default.qubit", "shots": None} -QDevice: TypeAlias = qml.Device -CDevice: TypeAlias = torch.device -DType: TypeAlias = torch.dtype -QNode: TypeAlias = qml.QNode -Tensor: TypeAlias = torch.Tensor -Wires: TypeAlias = Union[int, Iterable[Any]] -Expectation: TypeAlias = qml.measurements.ExpectationMP -Observable: TypeAlias = qml.operation.Observable -Observables: TypeAlias = Union[ - qml.operation.Observable, Iterable[qml.operation.Observable] -] -Probability: TypeAlias = qml.measurements.ProbabilityMP -Sample: TypeAlias = qml.measurements.SampleMP -Entropy: TypeAlias = qml.measurements.VnEntropyMP - class MeasurementType(Enum): """Measurement type for a measurement layer.""" @@ -189,9 +183,7 @@ def circuit(self, x: Tensor) -> None: N = len(Us) count = 0 for k in range(N - 1, -1, -1): - wires_idx = list( - range(self.num_wires - count - s - 1, self.num_wires - count) - ) + wires_idx = list(range(self.num_wires - count - s - 1, self.num_wires - count)) subwires = [self.wires[idx] for idx in wires_idx] qml.QubitUnitary(Us[k], wires=subwires, unitary_check=False) @@ -225,6 +217,149 @@ def compute_norm(self, x: Tensor) -> float: return self.norm +class Linear2DBasisQFE(CircuitLayer): + """ + Layer for the 2D hat basis quantum feature embedding. + + :param basis: The 1D hat basis class. + :type basis: HatBasis + :param wires: The wires to be used by the layer + :type wires: Wires + :param sqrt: Set flag to take square roots before applying hat basis. + :type sqrt: bool + :param normalize: Set flag to normalize basis vector before embedding. + :type normalize: bool + """ + + def __init__( + self, + wires: Wires, + basis: HatBasis, + sqrt: bool = False, + normalize: bool = False, + zorder: bool = False, + ) -> None: + super().__init__(wires) + self.basis = basis + self.sqrt = sqrt + self.normalize = normalize + self.norm = 1.0 + self.hbmps = HatBasisMPS(basis) + self.zorder = zorder + self.mps = None + self.mps1 = None + self.mps2 = None + + def circuit(self, x: Tensor) -> None: + """ + Define the quantum circuit for this layer. + + :param x: Input tensor that is passed to the quantum circuit. + :type x: Tensor + """ + self._check_input(x) + + x1 = x[0] + x2 = x[1] + position1 = int(self.basis.position(x1)) + position2 = int(self.basis.position(x2)) + a1, b1 = self.basis.nonz_vals(x1) + a2, b2 = self.basis.nonz_vals(x2) + + if self.sqrt: + # sometimes the values are close to 0 and negative + a1 = torch.sqrt(torch.abs(a1)) + b1 = torch.sqrt(torch.abs(b1)) + a2 = torch.sqrt(torch.abs(a2)) + b2 = torch.sqrt(torch.abs(b2)) + + # TODO: cover the case where x or y are outside of bounds + + val1 = a1 * a2 + val2 = a1 * b2 + val3 = a2 * b1 + val4 = a2 * b2 + norm = torch.sqrt(val1**2 + val2**2 + val3**2 + val4**2) + + if self.normalize: + a1 /= torch.sqrt(norm) + b1 /= torch.sqrt(norm) + a2 /= torch.sqrt(norm) + b2 /= torch.sqrt(norm) + + self.norm = norm.item() + + # for compatibility (TODO: remove) + first1 = a1.item() + second1 = b1.item() + first2 = a2.item() + second2 = b2.item() + + mps1 = self.hbmps.mps_hatbasis(first1, second1, position1) + mps2 = self.hbmps.mps_hatbasis(first2, second2, position2) + + if self.zorder: + mps = zkron(mps2, mps1) + else: + mps = kron(mps2, mps1) + + self.mps1 = mps1 + self.mps2 = mps2 + self.mps = mps + mpsgates = MPSQGates(mps) + + s = mpsgates.max_rank_power + Us = mpsgates.qgates() + N = len(Us) + count = 0 + for k in range(N - 1, -1, -1): + wires_idx = list(range(self.num_wires - count - s - 1, self.num_wires - count)) + subwires = [self.wires[idx] for idx in wires_idx] + qml.QubitUnitary(Us[k], wires=subwires, unitary_check=False) + + count += 1 + + def compute_norm(self, x: Tensor) -> float: + """ + Compute the norm of the basis vector for the given input x. + + :param x: Input tensor that is passed to basis vector. + :type x: Tensor + :returns: The norm. + :rtype: float + """ + self._check_input(x) + + x1 = x[0] + x2 = x[1] + a1, b1 = self.basis.nonz_vals(x1) + a2, b2 = self.basis.nonz_vals(x2) + + if self.sqrt: + # sometimes the values are close to 0 and negative + a1 = torch.sqrt(torch.abs(a1)) + b1 = torch.sqrt(torch.abs(b1)) + a2 = torch.sqrt(torch.abs(a2)) + b2 = torch.sqrt(torch.abs(b2)) + + # TODO: cover the case where x or y are outside of bounds + + val1 = a1 * a2 + val2 = a1 * b2 + val3 = a2 * b1 + val4 = a2 * b2 + self.norm = torch.sqrt(val1**2 + val2**2 + val3**2 + val4**2).item() + + return self.norm + + def _check_input(self, x: Tensor): + if x.dim() > 2: + raise ValueError("Input tensor must have 2 dimensions") + + if torch.any(torch.abs(x) >= 1): + raise ValueError("Out of bounds case is not implemented") + + class RYCZLayer(CircuitLayer): """ Layer for the RYCZ (Rotation around Y and Controlled-Z) gates. @@ -519,9 +654,7 @@ def __init__( num_var_repeats = self.num_varlayers for _ in range(self.num_repeat): - embed_layer = IQPEmbeddingLayer( - self.wires, self.num_uploads, **self.iqpe_opts - ) + embed_layer = IQPEmbeddingLayer(self.wires, self.num_uploads, **self.iqpe_opts) var_layers = [] for _ in range(num_var_repeats): @@ -605,9 +738,7 @@ def __init__( self.blocks = nn.ModuleList() for _ in range(self.num_repeat): - embed_layer = IQPEmbeddingLayer( - self.wires, self.num_uploads, **self.iqpe_opts - ) + embed_layer = IQPEmbeddingLayer(self.wires, self.num_uploads, **self.iqpe_opts) var_layer = AltRotCXLayer( self.wires, self.num_varlayers, @@ -693,11 +824,13 @@ def __init__( if not self.num_wires >= self.num_features: raise ValueError( - f"The number of wires ({self.num_wires}) must be greater than or equal to the number of features ({self.num_features})." + f"The number of wires ({self.num_wires}) " + f"must be greater than or equal to the number of features ({self.num_features})." ) if not self.num_wires % self.num_features == 0: raise ValueError( - f"The number of wires ({self.num_wires}) must be a multiple of the number of features ({self.num_features})." + f"The number of wires ({self.num_wires}) " + f"must be a multiple of the number of features ({self.num_features})." ) def circuit(self, x: Tensor) -> None: @@ -710,7 +843,8 @@ def circuit(self, x: Tensor) -> None: num_features = x.shape[-1] if num_features != self.num_features: raise ValueError( - f"Input tensor last dimension ({num_features}) must be equal to the number of features ({self.num_features})." + f"Input tensor last dimension ({num_features}) " + f"must be equal to the number of features ({self.num_features})." ) freq = 0 @@ -765,11 +899,13 @@ def __init__( if not self.num_wires >= self.num_features: raise ValueError( - f"The number of wires ({self.num_wires}) must be greater than or equal to the number of features ({self.num_features})." + f"The number of wires ({self.num_wires}) " + f"must be greater than or equal to the number of features ({self.num_features})." ) if not self.num_wires % self.num_features == 0: raise ValueError( - f"The number of wires ({self.num_wires}) must be a multiple of the number of features ({self.num_features})." + f"The number of wires ({self.num_wires}) " + f"must be a multiple of the number of features ({self.num_features})." ) def circuit(self, x: Tensor) -> None: @@ -782,7 +918,8 @@ def circuit(self, x: Tensor) -> None: num_features = x.shape[-1] if num_features != self.num_features: raise ValueError( - f"Input tensor last dimension ({num_features}) must be equal to the number of features ({self.num_features})." + f"Input tensor last dimension ({num_features}) " + f"must be equal to the number of features ({self.num_features})." ) num_repeats = int(self.num_wires / num_features) @@ -860,9 +997,7 @@ def circuit(self, _: Optional[Tensor] = None) -> None: for mps_layer_idx in range(self.n_layers_mps): for block_idx in ( - range(self.n_blocks - 1, -1, -1) - if self.reverse - else range(self.n_blocks) + range(self.n_blocks - 1, -1, -1) if self.reverse else range(self.n_blocks) ): self._block(mps_layer_idx, block_idx) @@ -1095,22 +1230,15 @@ def check_measurement_type(self) -> None: """ if not isinstance(self.measurement_type, MeasurementType): - raise NotImplementedError( - f"Measurement type ({self.measurement_type}) not recognized" - ) + raise NotImplementedError(f"Measurement type ({self.measurement_type}) not recognized") if self.measurement_type == MeasurementType.Expectation: if self.observables is None: raise ValueError( - f"Measurement type ({self.measurement_type}) " - "requires an observable" + f"Measurement type ({self.measurement_type}) " "requires an observable" ) - if ( - self.measurement_type == MeasurementType.Samples - and self.qdevice.shots is None - ): + if self.measurement_type == MeasurementType.Samples and self.qdevice.shots is None: raise ValueError( - f"Measurement type ({self.measurement_type}) " - "requires integer number of shots" + f"Measurement type ({self.measurement_type}) " "requires integer number of shots" ) diff --git a/qulearn/rademacher.py b/qulearn/rademacher.py index d4587cb..83cf743 100644 --- a/qulearn/rademacher.py +++ b/qulearn/rademacher.py @@ -1,30 +1,19 @@ -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - import torch -import pennylane as qml -from .datagen import DataGenRademacher +from .datagen import DataGenRademacher as Datagen from .loss import RademacherLoss -from .trainer import SupervisedTrainer - -Model: TypeAlias = qml.QNode -Tensor: TypeAlias = torch.Tensor -Trainer: TypeAlias = SupervisedTrainer -Datagen: TypeAlias = DataGenRademacher +from .trainer import SupervisedTrainer as Trainer +from .types import QModel, Tensor def rademacher( - model: Model, trainer: Trainer, X: Tensor, sigmas: Tensor, datagen: Datagen + model: QModel, trainer: Trainer, X: Tensor, sigmas: Tensor, datagen: Datagen ) -> Tensor: """ Estimate Rademacher complexity of a given model. :param model: Prediction model. - :type model: Model + :type model: QModel :param trainer: The trainer. :type trainer: Trainer :param X: Data tensor of size (num_data_samples, size_data_set, dim_feature) diff --git a/qulearn/trainer.py b/qulearn/trainer.py index 52c100a..df5cd9a 100644 --- a/qulearn/trainer.py +++ b/qulearn/trainer.py @@ -1,31 +1,11 @@ -from typing import Optional, Callable, Dict - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - from enum import Enum +from typing import Dict, Optional -import logging import torch from torch import nn -from torch.utils.tensorboard import SummaryWriter -from torch.utils.data import DataLoader -import pennylane as qml from .qkernel import QKernel - -Optimizer: TypeAlias = torch.optim.Optimizer -Loss: TypeAlias = torch.nn.Module -Metric: TypeAlias = Callable -Writer: TypeAlias = SummaryWriter -Logger: TypeAlias = logging.Logger -Model: TypeAlias = qml.QNode -Loader: TypeAlias = DataLoader -Tensor: TypeAlias = torch.Tensor -Parameter: TypeAlias = nn.Parameter +from .types import DataLoader, Logger, Loss, Metric, Model, Optimizer, Parameter, Tensor, Writer class EpochType(Enum): @@ -75,53 +55,51 @@ def __init__( self.writer = writer self.logger = logger - def train(self, model: Model, train_data: Loader, valid_data: Loader) -> None: + def train(self, model: Model, train_data: DataLoader, valid_data: DataLoader) -> None: """ Train the given model using the provided data loaders. :param model: The model to be trained. :type model: Model :param train_data: The DataLoader for the training data. - :type train_data: Loader + :type train_data: DataLoader :param valid_data: The DataLoader for the validation data. - :type valid_data: Loader + :type valid_data: DataLoader """ for epoch in range(1, self.num_epochs + 1): self.train_epoch(model, train_data, epoch) self.validate_epoch(model, valid_data, epoch) - def train_epoch(self, model: Model, train_data: Loader, epoch: int = 0) -> None: + def train_epoch(self, model: Model, train_data: DataLoader, epoch: int = 0) -> None: """ Train the model for one epoch. :param model: The model to be trained. :type model: Model :param train_data: The DataLoader for the training data. - :type train_data: Loader + :type train_data: DataLoader :param epoch: The current epoch number. Default is 0. :type epoch: int """ epoch_type = EpochType.Train self._epoch(epoch_type, model, train_data, epoch) - def validate_epoch(self, model: Model, valid_data: Loader, epoch: int = 0) -> None: + def validate_epoch(self, model: Model, valid_data: DataLoader, epoch: int = 0) -> None: """ Validate the model after an epoch of training. :param model: The model to be validated. :type model: Model :param valid_data: The DataLoader for the validation data. - :type valid_data: Loader + :type valid_data: DataLoader :param epoch: The current epoch number. Default is 0. :type epoch: int """ epoch_type = EpochType.Validate self._epoch(epoch_type, model, valid_data, epoch) - def _epoch( - self, epoch_type: EpochType, model: Model, data: Loader, epoch: int = 0 - ) -> None: + def _epoch(self, epoch_type: EpochType, model: Model, data: DataLoader, epoch: int = 0) -> None: running_loss = 0.0 running_metrics = {} for metric in self.metrics: @@ -154,15 +132,11 @@ def _train_step(self, model: Model, inputs: Tensor, labels: Tensor) -> None: loss.backward() self.optimizer.step() - def _log_metrics( - self, phase: str, loss: float, metrics: Dict[str, float], epoch: int - ) -> None: + def _log_metrics(self, phase: str, loss: float, metrics: Dict[str, float], epoch: int) -> None: if self.writer is not None: self.writer.add_scalar(f"Loss/{phase}", loss, epoch) for metric_name, metric_value in metrics.items(): - self.writer.add_scalar( - f"Metrics/{phase}/{metric_name}", metric_value, epoch - ) + self.writer.add_scalar(f"Metrics/{phase}/{metric_name}", metric_value, epoch) if self.logger is not None: metrics_strs = [ @@ -197,16 +171,16 @@ def __init__( self.metrics = metrics self.logger = logger - def train(self, model: QKernel, train_data: Loader, valid_data: Loader) -> None: + def train(self, model: QKernel, train_data: DataLoader, valid_data: DataLoader) -> None: """ Train the given model using the provided data loaders using Ridge Regression. :param model: The quantum kernel model to be trained. :type model: QKernel :param train_data: The DataLoader for the training data. - :type train_data: Loader + :type train_data: DataLoader :param valid_data: The DataLoader for the validation data. - :type valid_data: Loader + :type valid_data: DataLoader .. warning:: Training changes the state of the model by assigning `X_train`. @@ -237,9 +211,7 @@ def train(self, model: QKernel, train_data: Loader, valid_data: Loader) -> None: running_metrics[metric] = self.metrics[metric](predicted, labels) self._log_metrics(phase, running_metrics) - def kernel_ridge_regression( - self, model: QKernel, inputs: Tensor, labels: Tensor - ) -> Parameter: + def kernel_ridge_regression(self, model: QKernel, inputs: Tensor, labels: Tensor) -> Parameter: """ Compute Ridge Regression solution for the given inputs and labels using the provided model. @@ -258,8 +230,8 @@ def kernel_ridge_regression( K = model.kernel_matrix(inputs, inputs) num_samples = inputs.shape[0] - I = torch.eye(num_samples, dtype=labels.dtype, device=labels.device) - M = K + self.lambda_reg * I + Id = torch.eye(num_samples, dtype=labels.dtype, device=labels.device) + M = K + self.lambda_reg * Id alpha = nn.Parameter(torch.linalg.solve(M, labels)) return alpha diff --git a/qulearn/types.py b/qulearn/types.py new file mode 100644 index 0000000..b75e467 --- /dev/null +++ b/qulearn/types.py @@ -0,0 +1,47 @@ +from typing import Any, Callable, Dict, Iterable, List, Sequence, Tuple, Union + +# for python < 3.10 +try: + from typing import TypeAlias +except ImportError: + from typing_extensions import TypeAlias + +import logging + +import numpy as np +import pennylane as qml +import tntorch +import torch +from torch.utils.tensorboard import SummaryWriter + +# Type aliases +Tensor: TypeAlias = torch.Tensor +Array: TypeAlias = np.ndarray +Device: TypeAlias = torch.device +DataOut: TypeAlias = Dict[str, Tensor] +DataLoader: TypeAlias = torch.utils.data.DataLoader +TensorDataset: TypeAlias = torch.utils.data.TensorDataset +Model: TypeAlias = torch.nn.Module +QModel: TypeAlias = qml.QNode +ParameterList: TypeAlias = List[List[Tensor]] +CDevice: TypeAlias = torch.device +DType: TypeAlias = torch.dtype +Loss: TypeAlias = torch.nn.Module +Capacity = List[Tuple[int, float, int, int]] +MPS: TypeAlias = tntorch.tensor.Tensor +Observable: TypeAlias = qml.operation.Observable +Observables: TypeAlias = Union[qml.operation.Observable, Iterable[qml.operation.Observable]] +Probability: TypeAlias = qml.measurements.ProbabilityMP +Sample: TypeAlias = qml.measurements.SampleMP +Entropy: TypeAlias = qml.measurements.VnEntropyMP +Hamiltonian: TypeAlias = qml.Hamiltonian +ParitySequence: TypeAlias = Sequence[Tuple[int, ...]] +QDevice: TypeAlias = qml.Device +QNode: TypeAlias = qml.QNode +Expectation: TypeAlias = qml.measurements.ExpectationMP +Wires: TypeAlias = Union[int, Iterable[Any]] +Optimizer: TypeAlias = torch.optim.Optimizer +Metric: TypeAlias = Callable +Writer: TypeAlias = SummaryWriter +Logger: TypeAlias = logging.Logger +Parameter: TypeAlias = torch.nn.Parameter diff --git a/qulearn/utils.py b/qulearn/utils.py index 246ced7..38af52b 100644 --- a/qulearn/utils.py +++ b/qulearn/utils.py @@ -1,20 +1,10 @@ """Frequently used functions.""" -from typing import Dict, List, Tuple - -# for python < 3.10 -try: - from typing import TypeAlias -except ImportError: - from typing_extensions import TypeAlias - -from itertools import chain, combinations import math -import pennylane as qml -import torch +from itertools import chain, combinations +from typing import Dict, List, Tuple -Tensor: TypeAlias = torch.Tensor -Observable: TypeAlias = qml.Hamiltonian +from .types import Observable, Tensor def probabilities_to_dictionary(probs: Tensor) -> Dict[str, Tensor]: @@ -96,8 +86,7 @@ def parities_outcome(bitstring: str, H: Observable) -> float: if num_qubits != num_wires: raise ValueError( - f"Number of qubits ({num_qubits}) " - f"does not match number of wires ({num_wires})" + f"Number of qubits ({num_qubits}) " f"does not match number of wires ({num_wires})" ) sum = 0.0 @@ -125,9 +114,7 @@ def parities_outcome(bitstring: str, H: Observable) -> float: return sum -def parities_outcome_probs( - probs: Dict[str, float], H: Observable -) -> Dict[float, float]: +def parities_outcome_probs(probs: Dict[str, float], H: Observable) -> Dict[float, float]: """ Compute (real-valued) outputs with corresponding probabilities. diff --git a/scratch/scratch.ipynb b/scratch/scratch.ipynb index ec71416..00bfe53 100644 --- a/scratch/scratch.ipynb +++ b/scratch/scratch.ipynb @@ -12,7 +12,6 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], "source": [ "a = 0.3\n", "b = 0.5\n", @@ -35,34 +34,13 @@ " \n", " beta = beta1*(lam1+1)*(1+a-b)+beta2*(b-a)*(lam2+1)\n", " return beta" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -95,25 +73,13 @@ "ax.set_zlabel('effbeta')\n", "ax.set_title('Surface Plot of effbeta')\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "shape '[65, 1]' is invalid for input of size 9", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/mazen/Research/QC/QuLearn/scratch/scratch.ipynb Cell 4\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 38\u001b[0m \u001b[39m#all_combinations = list(itertools.product(*omega_spectrum))\u001b[39;00m\n\u001b[1;32m 39\u001b[0m all_combinations \u001b[39m=\u001b[39m omega_spectrum\n\u001b[0;32m---> 40\u001b[0m all_combinations \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mtensor(all_combinations, dtype\u001b[39m=\u001b[39;49mtorch\u001b[39m.\u001b[39;49mfloat64)\u001b[39m.\u001b[39;49mreshape(\u001b[39m65\u001b[39;49m, \u001b[39m1\u001b[39;49m)\n\u001b[1;32m 41\u001b[0m model \u001b[39m=\u001b[39m ClassicalSurrogate(id_z, all_combinations)\n\u001b[1;32m 43\u001b[0m \u001b[39m# Example forward pass:\u001b[39;00m\n", - "\u001b[0;31mRuntimeError\u001b[0m: shape '[65, 1]' is invalid for input of size 9" - ] - } - ], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -162,13 +128,13 @@ "output = model(x)\n", "print(output)\n", "print(output.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pennylane as qml\n", @@ -205,13 +171,13 @@ "plt.scatter(x, target_y, facecolor='white', edgecolor='black')\n", "plt.ylim(-1, 1)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "scaling = 1\n", "\n", @@ -247,24 +213,24 @@ "plt.plot(x, random_quantum_model_y, c='blue')\n", "#plt.ylim(-1,1)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "drawer = qml.draw_mpl(serial_quantum_model, show_all_wires=True, expansion_strategy=\"device\")\n", "x = 0.0\n", "print(drawer(weights, x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "def cost(weights, x, y):\n", " predictions = [serial_quantum_model(weights, x_) for x_ in x]\n", @@ -290,13 +256,13 @@ " cst.append(c)\n", " if (step + 1) % 10 == 0:\n", " print(\"Cost at step {0:3}: {1}\".format(step + 1, c))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "predictions = [serial_quantum_model(weights, x_) for x_ in x]\n", "\n", @@ -305,13 +271,13 @@ "plt.plot(x, predictions, c='blue')\n", "plt.ylim(-1,1)\n", "plt.show();" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import torch\n", @@ -328,13 +294,13 @@ "y = torch.tensor([-0.25], dtype=torch.float64)\n", "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "print(drawer())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "Z = np.array([[1, 0], [0, -1]])\n", @@ -345,13 +311,13 @@ "M = sum(matrices[i]*coefficients[i] for i in range(2))\n", "eigenvalues = np.linalg.eigvals(M)\n", "print(eigenvalues)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch.optim import Adam\n", "opt = Adam(model.parameters(), lr=0.1)\n", @@ -360,23 +326,23 @@ "loss = loss_fn(pred, y)\n", "loss.backward()\n", "opt.step()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "print(drawer())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "def scalar2vector(x, L):\n", " # Check if input is within the expected range\n", @@ -427,13 +393,13 @@ " print(\"less than 0\")\n", "if x > 0:\n", " print(\"larger than 0\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import CircuitLayer\n", "import pennylane as qml\n", @@ -452,13 +418,13 @@ " qml.PauliX(self.wires[index])\n", " \n", " qml.RZ(0.0, self.wires[-1])" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import CircuitLayer, AltRotCXLayer\n", "import pennylane as qml\n", @@ -592,13 +558,13 @@ " )\n", " self.qnode = qnode\n", " return self.qnode" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from enum import Enum\n", "class Test(Enum):\n", @@ -606,13 +572,13 @@ " two = 2\n", " \n", "print(Test.one.name)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "x1 = torch.tensor([[0.1]])\n", "x2 = torch.tensor([[0.2]])\n", @@ -620,13 +586,13 @@ "x = torch.cat((x1, x2, x3), dim=1)\n", "print(x)\n", "print(x.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn.qlayer import ParallelEntangledIQPEncoding, ParallelIQPEncoding, MeasurementLayer, MeasurementType, IQPERYCZLayer, RYCZLayer\n", @@ -678,43 +644,43 @@ "# loss_after = fn(predicted, labels)\n", "\n", "# print(f\"Loss before: {loss_before} | Loss after: {loss_after}\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(model.alpha)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "X_train = torch.randn((num_samples, num_features))\n", "model.X_train = X_train\n", "print(model.alpha)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for p in model.named_parameters():\n", " print(p)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import HamiltonianLayer, AltRotCXLayer, IQPEmbeddingLayer\n", "import torch\n", @@ -734,13 +700,13 @@ "print(y)\n", "x_ = x[0]\n", "print(drawer(x_))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import HamiltonianLayer, AltRotCXLayer, IQPEmbeddingLayer\n", "import torch\n", @@ -760,13 +726,13 @@ "y = model(x)\n", "print(y)\n", "print(drawer(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import torch\n", @@ -812,13 +778,13 @@ "x, y = generate_dataset(25, n, c)\n", "y = torch.where(x <= 0, torch.tensor(0, dtype=torch.float64), torch.tensor(1, dtype=torch.float64))\n", "dataloader = get_data_loader(x, y)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn.qlayer import MeasurementLayer, MeasurementType, IQPEAltRotCXLayer, HamiltonianLayer\n", @@ -832,13 +798,13 @@ "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "x = torch.randn(num_features)\n", "print(drawer(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch.optim import Adam\n", "from qulearn.trainer import SupervisedTrainer\n", @@ -848,56 +814,56 @@ "logger = logging.getLogger(\"SupTrainer\")\n", "logger.setLevel(level=logging.INFO)\n", "trainer = SupervisedTrainer(opt, loss_fn, num_epochs=50, logger=logger)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "trainer.train(model, dataloader, dataloader)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.fat import fat_shattering_dim\n", "from qulearn.datagen import DataGenFat, UniformPrior\n", "prior = UniformPrior(sizex=num_features, seed=0)\n", "gamma=0.1\n", "datagen = DataGenFat(prior=prior, Sb=10, Sr=5, gamma=2.0*gamma, seed=0, batch_size=25)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "dim = fat_shattering_dim(model, datagen=datagen, trainer=trainer, dmin=1, dmax=100, gamma=gamma, dstep=1)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.fat import check_shattering\n", "#check = check_shattering(model, datagen, trainer, 1, gamma=gamma)\n", "#print(check)\n", "print(dim)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(dim)\n", "data = datagen.gen_data(1)\n", @@ -908,13 +874,13 @@ "print(data[\"b\"])\n", "print(data[\"r\"])\n", "print(0.07260766+gamma*2)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -942,13 +908,13 @@ "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sympy import symbols, Matrix, cos, sin, exp\n", "from sympy.physics.quantum import TensorProduct as kron\n", @@ -998,13 +964,13 @@ "# Compute the expectation value of Z\n", "expectation = (psi.H * Z * psi)\n", "expectation[0]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sympy import symbols, Matrix, cos, sin, exp\n", "from sympy.physics.quantum import TensorProduct as kron\n", @@ -1065,13 +1031,13 @@ "# Compute the expectation value of Z\n", "expectation = (psi.H * Z * psi)\n", "expectation[0]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sympy import symbols, Matrix, cos, sin, exp\n", "from sympy.physics.quantum import TensorProduct as kron\n", @@ -1142,13 +1108,13 @@ "# Compute the expectation value of Z\n", "expectation = (psi.H * kron(Z, I) * psi)\n", "expectation[0]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -1203,13 +1169,13 @@ "plt.legend()\n", "plt.grid(True)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -1233,40 +1199,13 @@ "plt.grid(True)\n", "plt.legend()\n", "plt.show()\n" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([0.9639])\n", - "0: ──H──RZ(0.96)───Rot(5.73,1.50,0.92)─╭●──Rot(1.08,0.87,2.04)─────────────────────────╭●\n", - "1: ──H──RZ(1.93)───Rot(5.46,1.76,5.00)─╰X──Rot(0.33,0.36,3.38)─╭●──Rot(4.01,3.54,3.06)─╰X\n", - "2: ──H──RZ(3.86)───Rot(1.10,4.75,1.15)─╭●──Rot(4.41,2.58,2.66)─╰X──Rot(1.29,4.85,0.46)─╭●\n", - "3: ──H──RZ(7.71)───Rot(5.35,0.31,1.80)─╰X──Rot(1.78,1.01,4.78)─╭●──Rot(4.37,2.35,5.73)─╰X\n", - "4: ──H──RZ(15.42)──Rot(5.37,2.63,3.06)─────────────────────────╰X──Rot(6.20,1.32,5.91)───\n", - "\n", - "───Rot(3.56,4.46,5.36)─────────────────────────╭●──Rot(4.20,5.61,0.05)─────────────────────────┤\n", - "───Rot(2.90,4.84,1.06)─╭●──Rot(4.18,0.58,5.43)─╰X──Rot(4.70,3.59,1.99)─╭●──Rot(0.44,3.08,0.03)─┤\n", - "───Rot(5.06,5.36,4.85)─╰X──Rot(4.24,5.19,1.13)─╭●──Rot(0.74,1.40,1.66)─╰X──Rot(3.04,0.92,0.20)─┤\n", - "───Rot(3.98,1.64,3.79)─╭●──Rot(0.41,0.58,4.86)─╰X──Rot(0.91,3.47,6.02)─╭●──Rot(5.77,5.49,0.25)─┤\n", - "───────────────────────╰X──Rot(2.03,3.04,4.61)─────────────────────────╰X──Rot(4.77,1.81,2.89)─┤\n", - "\n", - " <𝓗(-0.15,0.80)>\n", - " \n", - " \n", - " \n", - " \n", - "89\n", - "{'gate_sizes': defaultdict(, {1: 34, 2: 12}), 'gate_types': defaultdict(, {'IQPEmbedding': 5, 'Rot': 29, 'CNOT': 12}), 'num_operations': 46, 'num_observables': 1, 'num_diagonalizing_gates': 0, 'num_used_wires': 5, 'depth': 14, 'num_trainable_params': 89, 'num_device_wires': 5, 'device_name': 'default.qubit.torch', 'expansion_strategy': 'gradient', 'gradient_options': {}, 'interface': 'torch', 'diff_method': 'backprop', 'gradient_fn': 'backprop'}\n" - ] - } - ], "source": [ "import pennylane as qml\n", "from qulearn.qlayer import AltRotCXLayer, ParallelEntangledIQPEncoding, ParallelIQPEncoding, MeasurementLayer, MeasurementType, IQPERYCZLayer, RYCZLayer, IQPEAltRotCXLayer, HadamardLayer, HamiltonianLayer, IQPEmbeddingLayer, AltRXCXLayer\n", @@ -1327,41 +1266,23 @@ "#print(model(x1))\n", "print(sum(p.numel() for p in model.parameters() if p.requires_grad))\n", "print(qml.specs(model.qnode)(x1))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(
, )\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "drawer = qml.draw_mpl(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "print(drawer(x1))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, - "outputs": [], "source": [ "from torch import nn\n", "class ExtendedModel(nn.Module):\n", @@ -1380,23 +1301,13 @@ " \n", "extended = ExtendedModel(model)\n", "model = extended" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-1.2338],\n", - " [ 3.6446]], dtype=torch.float64, grad_fn=)\n", - "torch.Size([2, 1])\n" - ] - } - ], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -1445,13 +1356,13 @@ "output = model(x)\n", "print(output)\n", "print(output.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# First, let's import necessary packages\n", "import torch\n", @@ -1486,13 +1397,13 @@ "print([p.shape for p in model.parameters() if p.requires_grad])\n", "print(sum(p.numel() for p in model.parameters() if p.requires_grad))\n", "print(model(x).shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torchviz import make_dot\n", "# Create a random tensor to represent a sample input\n", @@ -1501,13 +1412,13 @@ "\n", "# Visualize the network\n", "make_dot(y, params=dict(model.named_parameters())).render(\"network\", format=\"png\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "from qulearn.qlayer import MeasurementType\n", @@ -1520,13 +1431,13 @@ " entropies.append(model(x).item())\n", "max_entropies = [np.log2(min(2**len(subsystem), 2**(wires - len(subsystem)))) for subsystem in subsystems]\n", "print(x)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Plotting\n", "import matplotlib.pyplot as plt\n", @@ -1540,13 +1451,13 @@ "plt.grid(True)\n", "plt.title('Computed vs Maximum Entropy')\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -1650,24 +1561,13 @@ " return torch.sin(low * X) + 0.5 * torch.sin(high * X)\n", "\n", "Y_high_low = add_gaussian_noise(high_low(X))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "# Create the scatter plot\n", @@ -1675,13 +1575,13 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -1715,436 +1615,23 @@ " loss_fn=loss_fn,\n", " num_epochs=num_epochs,\n", " logger=logger)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:train_function:Train - Epoch: 1, Loss: 28.374617, Metrics: \n", - "INFO:train_function:Validate - Epoch: 1, Loss: 15.306045, Metrics: \n", - "INFO:train_function:Train - Epoch: 2, Loss: 6.944586, Metrics: \n", - "INFO:train_function:Validate - Epoch: 2, Loss: 4.070129, Metrics: \n", - "INFO:train_function:Train - Epoch: 3, Loss: 2.297779, Metrics: \n", - "INFO:train_function:Validate - Epoch: 3, Loss: 2.452321, Metrics: \n", - "INFO:train_function:Train - Epoch: 4, Loss: 4.190285, Metrics: \n", - "INFO:train_function:Validate - Epoch: 4, Loss: 2.566634, Metrics: \n", - "INFO:train_function:Train - Epoch: 5, Loss: 4.947295, Metrics: \n", - "INFO:train_function:Validate - Epoch: 5, Loss: 2.028602, Metrics: \n", - "INFO:train_function:Train - Epoch: 6, Loss: 3.355003, Metrics: \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:train_function:Validate - Epoch: 6, Loss: 1.606066, Metrics: \n", - "INFO:train_function:Train - Epoch: 7, Loss: 1.463900, Metrics: \n", - "INFO:train_function:Validate - Epoch: 7, Loss: 1.601547, Metrics: \n", - "INFO:train_function:Train - Epoch: 8, Loss: 0.589338, Metrics: \n", - "INFO:train_function:Validate - Epoch: 8, Loss: 1.405363, Metrics: \n", - "INFO:train_function:Train - Epoch: 9, Loss: 0.506256, Metrics: \n", - "INFO:train_function:Validate - Epoch: 9, Loss: 0.881238, 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"INFO:train_function:Validate - Epoch: 23, Loss: 0.120179, Metrics: \n", - "INFO:train_function:Train - Epoch: 24, Loss: 0.032701, Metrics: \n", - "INFO:train_function:Validate - Epoch: 24, Loss: 0.103704, Metrics: \n", - "INFO:train_function:Train - Epoch: 25, Loss: 0.031135, Metrics: \n", - "INFO:train_function:Validate - Epoch: 25, Loss: 0.095953, Metrics: \n", - "INFO:train_function:Train - Epoch: 26, Loss: 0.029130, Metrics: \n", - "INFO:train_function:Validate - Epoch: 26, Loss: 0.095093, Metrics: \n", - "INFO:train_function:Train - Epoch: 27, Loss: 0.028071, Metrics: \n", - "INFO:train_function:Validate - Epoch: 27, Loss: 0.096863, Metrics: \n", - "INFO:train_function:Train - Epoch: 28, Loss: 0.027399, Metrics: \n", - "INFO:train_function:Validate - Epoch: 28, Loss: 0.095346, Metrics: \n", - "INFO:train_function:Train - Epoch: 29, Loss: 0.026354, Metrics: \n", - "INFO:train_function:Validate - Epoch: 29, Loss: 0.088803, Metrics: \n", - "INFO:train_function:Train - Epoch: 30, 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"INFO:train_function:Train - Epoch: 37, Loss: 0.020551, Metrics: \n", - "INFO:train_function:Validate - Epoch: 37, Loss: 0.063997, Metrics: \n", - "INFO:train_function:Train - Epoch: 38, Loss: 0.019991, Metrics: \n", - "INFO:train_function:Validate - Epoch: 38, Loss: 0.061732, Metrics: \n", - "INFO:train_function:Train - Epoch: 39, Loss: 0.019457, Metrics: \n", - "INFO:train_function:Validate - Epoch: 39, Loss: 0.060039, Metrics: \n", - "INFO:train_function:Train - Epoch: 40, Loss: 0.018946, Metrics: \n", - "INFO:train_function:Validate - Epoch: 40, Loss: 0.058586, Metrics: \n", - "INFO:train_function:Train - Epoch: 41, Loss: 0.018462, Metrics: \n", - "INFO:train_function:Validate - Epoch: 41, Loss: 0.057132, Metrics: \n", - "INFO:train_function:Train - Epoch: 42, Loss: 0.018004, Metrics: \n", - "INFO:train_function:Validate - Epoch: 42, Loss: 0.055635, Metrics: \n", - "INFO:train_function:Train - Epoch: 43, Loss: 0.017567, Metrics: \n", - "INFO:train_function:Validate - Epoch: 43, 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Metrics: \n", - "INFO:train_function:Train - Epoch: 138, Loss: 0.002960, Metrics: \n", - "INFO:train_function:Validate - Epoch: 138, Loss: 0.008934, Metrics: \n", - "INFO:train_function:Train - Epoch: 139, Loss: 0.002900, Metrics: \n", - "INFO:train_function:Validate - Epoch: 139, Loss: 0.008760, Metrics: \n", - "INFO:train_function:Train - Epoch: 140, Loss: 0.002842, Metrics: \n", - "INFO:train_function:Validate - Epoch: 140, Loss: 0.008588, Metrics: \n", - "INFO:train_function:Train - Epoch: 141, Loss: 0.002784, Metrics: \n", - "INFO:train_function:Validate - Epoch: 141, Loss: 0.008420, Metrics: \n", - "INFO:train_function:Train - Epoch: 142, Loss: 0.002728, Metrics: \n", - "INFO:train_function:Validate - Epoch: 142, Loss: 0.008255, Metrics: \n", - "INFO:train_function:Train - Epoch: 143, Loss: 0.002672, Metrics: \n", - "INFO:train_function:Validate - Epoch: 143, Loss: 0.008092, Metrics: \n", - "INFO:train_function:Train - Epoch: 144, Loss: 0.002618, Metrics: \n", - "INFO:train_function:Validate - Epoch: 144, Loss: 0.007933, Metrics: \n", - "INFO:train_function:Train - Epoch: 145, Loss: 0.002565, Metrics: \n", - "INFO:train_function:Validate - Epoch: 145, Loss: 0.007776, Metrics: \n", - "INFO:train_function:Train - Epoch: 146, Loss: 0.002512, Metrics: \n", - "INFO:train_function:Validate - Epoch: 146, Loss: 0.007622, Metrics: \n", - "INFO:train_function:Train - Epoch: 147, Loss: 0.002461, Metrics: \n", - "INFO:train_function:Validate - Epoch: 147, Loss: 0.007471, Metrics: \n", - "INFO:train_function:Train - Epoch: 148, Loss: 0.002411, Metrics: \n", - "INFO:train_function:Validate - Epoch: 148, Loss: 0.007323, Metrics: \n", - "INFO:train_function:Train - Epoch: 149, Loss: 0.002361, Metrics: \n", - "INFO:train_function:Validate - Epoch: 149, Loss: 0.007177, Metrics: \n", - "INFO:train_function:Train - Epoch: 150, Loss: 0.002313, Metrics: \n", - "INFO:train_function:Validate - Epoch: 150, Loss: 0.007034, Metrics: \n", - "INFO:train_function:Train - Epoch: 151, Loss: 0.002265, Metrics: \n", - "INFO:train_function:Validate - Epoch: 151, Loss: 0.006894, Metrics: \n", - "INFO:train_function:Train - Epoch: 152, Loss: 0.002218, Metrics: \n", - "INFO:train_function:Validate - Epoch: 152, Loss: 0.006756, Metrics: \n", - "INFO:train_function:Train - Epoch: 153, Loss: 0.002173, Metrics: \n", - "INFO:train_function:Validate - Epoch: 153, Loss: 0.006620, Metrics: \n", - "INFO:train_function:Train - Epoch: 154, Loss: 0.002128, Metrics: \n", - "INFO:train_function:Validate - Epoch: 154, Loss: 0.006488, Metrics: \n", - "INFO:train_function:Train - Epoch: 155, Loss: 0.002084, Metrics: \n", - "INFO:train_function:Validate - Epoch: 155, Loss: 0.006357, Metrics: \n", - "INFO:train_function:Train - Epoch: 156, Loss: 0.002040, Metrics: \n", - "INFO:train_function:Validate - Epoch: 156, Loss: 0.006230, Metrics: \n", - "INFO:train_function:Train - Epoch: 157, Loss: 0.001998, Metrics: \n", - "INFO:train_function:Validate - Epoch: 157, Loss: 0.006104, Metrics: \n", - "INFO:train_function:Train - Epoch: 158, Loss: 0.001956, Metrics: \n", - "INFO:train_function:Validate - Epoch: 158, Loss: 0.005981, Metrics: \n", - "INFO:train_function:Train - Epoch: 159, Loss: 0.001916, Metrics: \n", - "INFO:train_function:Validate - Epoch: 159, Loss: 0.005860, Metrics: \n", - "INFO:train_function:Train - Epoch: 160, Loss: 0.001876, Metrics: \n", - "INFO:train_function:Validate - Epoch: 160, Loss: 0.005742, Metrics: \n", - "INFO:train_function:Train - Epoch: 161, Loss: 0.001837, Metrics: \n", - "INFO:train_function:Validate - Epoch: 161, Loss: 0.005626, Metrics: \n", - "INFO:train_function:Train - Epoch: 162, Loss: 0.001798, Metrics: \n", - "INFO:train_function:Validate - Epoch: 162, Loss: 0.005512, Metrics: \n", - "INFO:train_function:Train - Epoch: 163, Loss: 0.001761, Metrics: \n", - "INFO:train_function:Validate - Epoch: 163, Loss: 0.005400, Metrics: \n", - "INFO:train_function:Train - Epoch: 164, Loss: 0.001724, Metrics: \n", - "INFO:train_function:Validate - Epoch: 164, Loss: 0.005290, Metrics: \n", - "INFO:train_function:Train - Epoch: 165, Loss: 0.001688, Metrics: \n", - "INFO:train_function:Validate - Epoch: 165, Loss: 0.005183, Metrics: \n", - "INFO:train_function:Train - Epoch: 166, Loss: 0.001652, Metrics: \n", - "INFO:train_function:Validate - Epoch: 166, Loss: 0.005077, Metrics: \n", - "INFO:train_function:Train - Epoch: 167, Loss: 0.001618, Metrics: \n", - "INFO:train_function:Validate - Epoch: 167, Loss: 0.004974, Metrics: \n", - "INFO:train_function:Train - Epoch: 168, Loss: 0.001584, Metrics: \n", - "INFO:train_function:Validate - Epoch: 168, Loss: 0.004872, Metrics: \n", - "INFO:train_function:Train - Epoch: 169, Loss: 0.001550, Metrics: \n", - "INFO:train_function:Validate - Epoch: 169, Loss: 0.004773, Metrics: \n", - "INFO:train_function:Train - Epoch: 170, Loss: 0.001518, Metrics: \n", - "INFO:train_function:Validate - Epoch: 170, Loss: 0.004675, Metrics: \n", - "INFO:train_function:Train - Epoch: 171, Loss: 0.001486, Metrics: \n", - "INFO:train_function:Validate - Epoch: 171, Loss: 0.004580, Metrics: \n", - "INFO:train_function:Train - Epoch: 172, Loss: 0.001454, Metrics: \n", - "INFO:train_function:Validate - Epoch: 172, Loss: 0.004486, Metrics: \n", - "INFO:train_function:Train - Epoch: 173, Loss: 0.001424, Metrics: \n", - "INFO:train_function:Validate - Epoch: 173, Loss: 0.004394, Metrics: \n", - "INFO:train_function:Train - Epoch: 174, Loss: 0.001394, Metrics: \n", - "INFO:train_function:Validate - Epoch: 174, Loss: 0.004304, Metrics: \n", - "INFO:train_function:Train - Epoch: 175, Loss: 0.001364, Metrics: \n", - "INFO:train_function:Validate - Epoch: 175, Loss: 0.004216, Metrics: \n", - "INFO:train_function:Train - Epoch: 176, Loss: 0.001336, Metrics: \n", - "INFO:train_function:Validate - Epoch: 176, Loss: 0.004129, Metrics: \n", - "INFO:train_function:Train - Epoch: 177, Loss: 0.001307, Metrics: \n", - "INFO:train_function:Validate - Epoch: 177, Loss: 0.004044, Metrics: \n", - "INFO:train_function:Train - Epoch: 178, Loss: 0.001280, Metrics: \n", - "INFO:train_function:Validate - Epoch: 178, Loss: 0.003961, Metrics: \n", - "INFO:train_function:Train - Epoch: 179, Loss: 0.001253, Metrics: \n", - "INFO:train_function:Validate - Epoch: 179, Loss: 0.003880, Metrics: \n", - "INFO:train_function:Train - Epoch: 180, Loss: 0.001226, Metrics: \n", - "INFO:train_function:Validate - Epoch: 180, Loss: 0.003800, Metrics: \n", - "INFO:train_function:Train - Epoch: 181, Loss: 0.001200, Metrics: \n", - "INFO:train_function:Validate - Epoch: 181, Loss: 0.003722, Metrics: \n", - "INFO:train_function:Train - Epoch: 182, Loss: 0.001175, Metrics: \n", - "INFO:train_function:Validate - Epoch: 182, Loss: 0.003645, Metrics: \n", - "INFO:train_function:Train - Epoch: 183, Loss: 0.001150, Metrics: \n", - "INFO:train_function:Validate - Epoch: 183, Loss: 0.003570, Metrics: \n", - "INFO:train_function:Train - Epoch: 184, Loss: 0.001126, Metrics: \n", - "INFO:train_function:Validate - Epoch: 184, Loss: 0.003496, Metrics: \n", - "INFO:train_function:Train - Epoch: 185, Loss: 0.001102, Metrics: \n", - "INFO:train_function:Validate - Epoch: 185, Loss: 0.003424, Metrics: \n", - "INFO:train_function:Train - Epoch: 186, Loss: 0.001079, Metrics: \n", - "INFO:train_function:Validate - Epoch: 186, Loss: 0.003354, Metrics: \n", - "INFO:train_function:Train - Epoch: 187, Loss: 0.001056, Metrics: \n", - "INFO:train_function:Validate - Epoch: 187, Loss: 0.003285, Metrics: \n", - "INFO:train_function:Train - Epoch: 188, Loss: 0.001034, Metrics: \n", - "INFO:train_function:Validate - Epoch: 188, Loss: 0.003217, Metrics: \n", - "INFO:train_function:Train - Epoch: 189, Loss: 0.001012, Metrics: \n", - "INFO:train_function:Validate - Epoch: 189, Loss: 0.003150, Metrics: \n", - "INFO:train_function:Train - Epoch: 190, Loss: 0.000990, Metrics: \n", - "INFO:train_function:Validate - Epoch: 190, Loss: 0.003085, Metrics: \n", - "INFO:train_function:Train - Epoch: 191, Loss: 0.000969, Metrics: \n", - "INFO:train_function:Validate - Epoch: 191, Loss: 0.003022, Metrics: \n", - "INFO:train_function:Train - Epoch: 192, Loss: 0.000949, Metrics: \n", - "INFO:train_function:Validate - Epoch: 192, Loss: 0.002959, Metrics: \n", - "INFO:train_function:Train - Epoch: 193, Loss: 0.000929, Metrics: \n", - "INFO:train_function:Validate - Epoch: 193, Loss: 0.002898, Metrics: \n", - "INFO:train_function:Train - Epoch: 194, Loss: 0.000909, Metrics: \n", - "INFO:train_function:Validate - Epoch: 194, Loss: 0.002839, Metrics: \n", - "INFO:train_function:Train - Epoch: 195, Loss: 0.000890, Metrics: \n", - "INFO:train_function:Validate - Epoch: 195, Loss: 0.002780, Metrics: \n", - "INFO:train_function:Train - Epoch: 196, Loss: 0.000872, Metrics: \n", - "INFO:train_function:Validate - Epoch: 196, Loss: 0.002723, Metrics: \n", - "INFO:train_function:Train - Epoch: 197, Loss: 0.000853, Metrics: \n", - "INFO:train_function:Validate - Epoch: 197, Loss: 0.002667, Metrics: \n", - "INFO:train_function:Train - Epoch: 198, Loss: 0.000835, Metrics: \n", - "INFO:train_function:Validate - Epoch: 198, Loss: 0.002612, Metrics: \n", - "INFO:train_function:Train - Epoch: 199, Loss: 0.000818, Metrics: \n", - "INFO:train_function:Validate - Epoch: 199, Loss: 0.002558, Metrics: \n", - "INFO:train_function:Train - Epoch: 200, Loss: 0.000801, Metrics: \n", - "INFO:train_function:Validate - Epoch: 200, Loss: 0.002505, Metrics: \n" - ] - } - ], "source": [ "# Train\n", "trainer.train(model, train_data=loader_train, valid_data=loader_valid)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.trainer import RidgeRegression\n", "lambda_reg = 1e-01\n", @@ -2154,24 +1641,13 @@ "metrics = {\"mse_loss\": loss_fn}\n", "trainer = RidgeRegression(lambda_reg=lambda_reg, metrics=metrics, logger=logger)\n", "trainer.train(model, loader_train, loader_train)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Plotting\n", "X = torch.linspace(-1, 1, 1000, dtype=torch.float64).reshape(-1, 1)\n", @@ -2193,24 +1669,13 @@ "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -2240,44 +1705,44 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "outputs" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import sys\n", "sys.path.append(\"/home/mazen/Research/QC/QuLearn/examples/compare_models\")\n", "from model_builder import QNNModel, QNNStatModel" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "model = QNNStatModel(num_features=1, num_reuploads=1, num_varlayers=1, num_repeats=1, omega=0.0, double_wires=False, id=\"0\")\n", "for p in model.parameters():\n", " print(p)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import torch\n", @@ -2285,13 +1750,13 @@ "x = torch.randn(1, 1)\n", "print(model(x))\n", "print(drawer(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -2332,13 +1797,13 @@ " plt.xlabel('x')\n", " plt.ylabel('y')\n", " plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "\n", @@ -2354,13 +1819,13 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import qmc\n", @@ -2387,13 +1852,13 @@ " parameter_list = [[torch.tensor(samples[j][i], device=None, dtype=None) for j in range(len(samples))] for i in range(n_samples)]\n", "\n", " return parameter_list" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "test = generate_model_samples(model, 5)\n", "print(len(test))\n", @@ -2401,47 +1866,47 @@ " print(len(t))\n", " for x in t:\n", " print(x.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for input, label in dataloader:\n", " print(input)\n", " print(label)\n", " break" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import logging\n", "logger = logging.getLogger(__name__)\n", "logger.setLevel(level=logging.INFO)\n", "logger.info(\"hello world\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "print(torch.__version__)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import IQPEmbeddingLayer\n", "from qulearn.qlayer import QEvalType\n", @@ -2459,13 +1924,13 @@ "out = qnode()\n", "print(out)\n", "print(qdev.shots)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import math\n", @@ -2476,13 +1941,13 @@ "a = [0, 1]\n", "b = [0, 2]\n", "print(a==b)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import IQPEmbeddingLayer, RYCZLayer, MeasurementLayer, HamiltonianLayer, MeasurementType, EmbedVarLayer, CircuitLayer\n", "import pennylane as qml\n", @@ -2507,24 +1972,24 @@ "x = torch.randn(3, num_wires)\n", "drawer = qml.draw_mpl(ham_layer.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "drawer(x)\n" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "y = torch.cat((x, x), dim=1)\n", "print(x)\n", "print(y)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.utils import all_bin_sequences\n", "from qulearn.observable import sequence2parity_observable\n", @@ -2539,44 +2004,44 @@ "print(all_obs)\n", "print(pairs_obs)\n", "all_obs[0].name" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(tuple(range(3)))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for p in ham_layer.parameters():\n", " print(p)\n", "print(ham_layer.observable.coeffs)\n", "print(ham_layer.observable)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "tmp = torch.tensor([1., -1., 0.5])\n", "print(tmp.numel())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.fim import empirical_fim, compute_fims, mc_integrate_idfim_det\n", "\n", @@ -2591,33 +2056,33 @@ " plist.append([p1[i], p2[i]])\n", "\n", "FIMs = compute_fims(measure_layer, x, plist)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "integral = mc_integrate_idfim_det(FIMs, 1.0, 1.0)\n", "print(integral)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "lin = torch.nn.Linear(3, 1)\n", "print(lin(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "y1 = upload_layer(x)\n", "y2 = var_layer(x)\n", @@ -2626,13 +2091,13 @@ "\n", "print(y3)\n", "print(y4)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "print(y3)\n", @@ -2649,13 +2114,13 @@ "]\n", "grad = torch.cat(grad_list)\n", "prod = torch.outer(grad, grad)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for key, val in measure_layer.state_dict().items():\n", " print(key)\n", @@ -2668,13 +2133,13 @@ " print(key)\n", " print(val)\n", " print(\"====\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch import nn\n", "class HybridModel(nn.Module):\n", @@ -2700,34 +2165,34 @@ " print(key)\n", " print(val)\n", " print(\"======\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(y)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for key, val in measure_layer.state_dict().items():\n", " print(key)\n", " print(val)\n", " print(\"=====\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from enum import Enum\n", "\n", @@ -2744,13 +2209,13 @@ "\n", "if not isinstance(y, MyType):\n", " raise NotImplementedError(\"QEvalType not implemented\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "from torch.nn import Linear\n", @@ -2758,13 +2223,13 @@ "X = torch.tensor([[0.1, 1.1, -2.2], [0.6, 4.1, -3.2], [-0.1, -2.1, -2.2]])\n", "Y = model(X)\n", "print(Y.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/test\")\n", @@ -2773,13 +2238,13 @@ " writer.add_scalar(\"foobar1\", val, i)\n", " writer.add_scalars(\"loss\", {\"train\": val, \"valid\": val+1.0}, i)\n", "writer.close()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import pennylane as qml\n", @@ -2812,13 +2277,13 @@ "loss = torch.nn.MSELoss()\n", "test = loss(Ypred, Ypred)\n", "print(test)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch.nn import Parameter\n", "P = model.parameters()\n", @@ -2847,13 +2312,13 @@ " print(p)\n", "print(\"====================\")\n", "print(save)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import os\n", "os.environ[\"OMP_NUM_THREADS\"] = \"8\"\n", @@ -2877,13 +2342,13 @@ "\n", "x = torch.zeros(3, num_wires)\n", "print(ham_layer(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "from torch.utils.data import DataLoader, TensorDataset\n", @@ -2901,13 +2366,13 @@ "\n", "dataset = TensorDataset(X, Y)\n", "loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch.optim import Adam\n", "from qulearn.trainer import SupervisedTrainer\n", @@ -2920,62 +2385,62 @@ "logger = logging.getLogger(\"SupTrainer\")\n", "logger.setLevel(level=logging.INFO)\n", "trainer = SupervisedTrainer(opt, loss_fn, metrics, 200, logger=logger)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "trainer.train(model, loader, loader)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "%%timeit\n", "trainer.train(ham_layer, loader, loader) # lightning + torch + adjoint (+ omp-num-threads=8)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "%%timeit\n", "trainer.train(ham_layer, loader, loader) # default + torch + backprop" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "%%timeit\n", "trainer.train(ham_layer, loader, loader) # default + torch + adjoint" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "%lprun -T lprof0 -u 1.0 -f trainer._train_step trainer.train(ham_layer, loader, loader)\n", "writer.close()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from enum import Enum\n", "\n", @@ -2991,13 +2456,13 @@ "\n", "example = MeasurementType.Expectation\n", "print(type(example.name))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qml_mor.trainer import RegressionTrainer\n", "from torch.utils.tensorboard import SummaryWriter\n", @@ -3012,13 +2477,13 @@ "loss = training.train(model, loader, loader)\n", "print(loss)\n", "writer.close()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "path = \"model_bestmre\"\n", "state = torch.load(path)\n", @@ -3027,23 +2492,23 @@ "print(Y)\n", "print(\"==============\")\n", "print(Ypred)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "%load_ext tensorboard\n", "%tensorboard --logdir runs/fashion_trainer_20230517_113403" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import pennylane as qml\n", @@ -3090,13 +2555,13 @@ "loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n", "\n", "opt_params = opt.optimize(qnn_model, loader)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "Nx = X.size(0)\n", "y_pred = torch.stack([qnn_model(X[k], opt_params) for k in range(Nx)])\n", @@ -3104,13 +2569,13 @@ "\n", "print(mre)\n", "print(loss_fn(y_pred, Y))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "from torch.utils.data import TensorDataset, DataLoader\n", @@ -3130,26 +2595,26 @@ " print(X_)\n", " print(Y_)\n", " print(\"***********\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qml_mor.datagen import DataGenRademacher, NormalPrior\n", "prior = NormalPrior(3, seed=0)\n", "radem = DataGenRademacher(prior, 2, 3, seed=None)\n", "data = radem.gen_data(4)\n", "print(data)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import pennylane as qml\n", @@ -3163,20 +2628,20 @@ "print(H.coeffs.requires_grad)\n", "W_ = torch.nn.Parameter()\n", "print(W_)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "source": [], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import torch\n", @@ -3254,13 +2719,13 @@ "#print(drawer(x, init_theta, theta, W))\n", "probs = qnode(x, init_theta, theta, W, omega)\n", "probs" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "samples = probs\n", "bitstrings = [''.join(str(b.item()) for b in sample) for sample in samples]\n", @@ -3268,13 +2733,13 @@ "print(samples)\n", "print(bitstrings)\n", "print(bitstring_counts)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qml_mor.models import IQPEReuploadSU2Parity\n", "\n", @@ -3284,13 +2749,13 @@ "probs = qnode(x, params)\n", "print(probs)\n", "print(model.Hamiltonian(params))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "H = model.Hamiltonian(params)\n", "sum = 0.0\n", @@ -3316,24 +2781,24 @@ " sign *= (-1)**(int(b[-1-w]))\n", "\n", " sum += sign*H.coeffs[idx]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(probs)\n", "marginal = qml.math.marginal_prob(probs, axis=[0])\n", "print(marginal)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qml_mor.models import parities\n", "\n", @@ -3342,7 +2807,8 @@ "H = qml.Hamiltonian(W, test)\n", "print(H)\n", "print(H.ops)" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -3356,30 +2822,29 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# feature layer\n", "def feature_layer(x):\n", " num_qubits = len(x)\n", " qml.IQPEmbedding(x, wires=range(num_qubits))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# variational layer\n", "def variational_layer(init_theta, theta, num_qubits):\n", " qml.SimplifiedTwoDesign(initial_layer_weights=init_theta, weights=theta, wires=range(num_qubits))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# observable / output layer\n", "def sequence_generator(n):\n", @@ -3408,13 +2873,13 @@ " ops.append(qml.Identity(0))\n", "\n", " return ops" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from pennylane.templates import IQPEmbedding, SimplifiedTwoDesign\n", "# QNN model\n", @@ -3445,13 +2910,13 @@ " H = qml.Hamiltonian(W, obs)\n", "\n", " return qml.expval(H)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from pennylane.templates import IQPEmbedding\n", @@ -3471,13 +2936,13 @@ "features = np.random.random((n_layers, n_wires))\n", "\n", "result1 = iqpe_circuit(features)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "num_qubits = 3\n", "num_reps = 0\n", @@ -3493,13 +2958,13 @@ "\n", "ret = torch.stack([result1, result2])\n", "result1.size()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from scipy.stats import qmc\n", "def generate_samples_r(d, S):\n", @@ -3519,13 +2984,13 @@ "arr = np.array(list(tmp))\n", "print(arr)\n", "arr[0, 3]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -3540,24 +3005,24 @@ "tensor_list *= 2\n", "print(tensor_list)\n", "print(est_params)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "msg = (f\"Stopping early\\n\"\n", " \"Loss not improving\")\n", "print(msg)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import torch\n", @@ -3606,13 +3071,13 @@ " print(f\"Epoch {epoch + 1}, Loss: {loss.item()}\")\n", "\n", "print(f\"Trained parameters: {params}\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "num_qubits = 3\n", "num_reps = 3\n", @@ -3651,25 +3116,25 @@ "\n", "result = qnn_model(x, init_theta, theta, W)\n", "print(result.shape())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "tmp = parities(3)\n", "for x in tmp:\n", " print(type(x))\n", " print(issubclass(type(x), qml.operation.Observable))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# example\n", "num_qubits = 3\n", @@ -3682,7 +3147,8 @@ "W = torch.randn(2**num_qubits, requires_grad=True)\n", "\n", "print(qml.draw_mpl(qnn_model)(x, init_theta, theta, W, omega))" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -3696,7 +3162,6 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "def gen_dataset(N, samples=10, seed=0):\n", " sizex = num_qubits\n", @@ -3708,7 +3173,8 @@ " y = scale*torch.rand(samples, N, requires_grad=False) + shift\n", "\n", " return x, y" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -3722,29 +3188,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# model specs\n", "num_qubits = 3\n", "num_reps = 2\n", "num_layers = 2\n", "omega = 1." - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import this" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# initial parameters\n", "seed = 0\n", @@ -3752,13 +3217,13 @@ "init_theta = torch.randn(num_reps, num_qubits, requires_grad=True)\n", "theta = torch.randn(num_reps, num_layers, num_qubits-1, 2, requires_grad=True)\n", "W = torch.randn(2**num_qubits, requires_grad=True)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# loss function\n", "def square_loss(targets, predictions):\n", @@ -3767,13 +3232,13 @@ " loss += (t - p) ** 2\n", " loss = loss / len(targets)\n", " return 0.5*loss" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# capacity estimation parameters\n", "Nmin = 1\n", @@ -3781,13 +3246,13 @@ "samples = 10\n", "steps = 300\n", "eps_stop = 1e-12" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "summary = {}\n", "for N in range(Nmin, Nmax):\n", @@ -3822,30 +3287,31 @@ "\n", " mre_N = torch.mean(torch.tensor(mre_sample))\n", " summary[f'N = {N}'] = mre_N.item()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "for count, eps in enumerate(summary.values()):\n", " m = int(np.log2(1./eps.item()))\n", " C = (count+1)*m\n", " print(C)\n", "print(torch.numel(init_theta))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "C = [-0.5, 13.1, 2]\n", "max(C)" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -3859,30 +3325,29 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "y_pred = torch.tensor([qnn_model(x[k], init_theta, theta, W) for k in range(N)], requires_grad=False)\n", "mre = torch.mean(torch.abs((y[s]-y_pred)/y_pred))\n", "print(mre.item())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# cutoff precision converted to bits of precision\n", "cutoff = np.sqrt(eps_stop)\n", "m = np.log2(1./cutoff)\n", "print(m)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "print(x)\n", "print(init_theta)\n", @@ -3890,7 +3355,8 @@ "print(W)\n", "print(y[s])\n", "print(y_pred)" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/scratch/scratch2.ipynb b/scratch/scratch2.ipynb index e8fd04d..f05cc17 100644 --- a/scratch/scratch2.ipynb +++ b/scratch/scratch2.ipynb @@ -4,7 +4,6 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import torch\n", @@ -30,23 +29,13 @@ "print(x_train.shape)\n", "print(y.shape)\n", "print(y)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Left Points: tensor([ 0.5000, -0.5000, 0.5000])\n", - "Right Points: tensor([1., 0., 1.])\n", - "Position: tensor([3., 1., 3.])\n" - ] - } - ], "source": [ "import torch\n", "\n", @@ -78,23 +67,13 @@ "print(\"Left Points:\", left_points)\n", "print(\"Right Points:\", right_points)\n", "print(\"Position:\", position)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.8660, 0.5000, 0.7071, 0.7071, 1.0000, 0.0000, 0.0000, 1.0000]],\n", - " dtype=torch.float64)\n", - "torch.Size([1, 8])\n" - ] - } - ], "source": [ "import torch\n", "\n", @@ -143,23 +122,13 @@ "n = 3\n", "print(sawtooth_vector(x, n))\n", "print(sawtooth_vector(x, n).shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.5000, 0.5000],\n", - " [0.2500, 0.7500]], dtype=torch.float64)\n", - "torch.Size([2, 2])\n" - ] - } - ], "source": [ "import numpy as np\n", "import torch\n", @@ -190,32 +159,13 @@ "result = linear_FEM_basis(x_tensor, n)\n", "print(result)\n", "print(result.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TT cores [tensor([[[0., 1.],\n", - " [1., 0.]]], dtype=torch.float64), tensor([[[ 1., 0.],\n", - " [ 0., 0.]],\n", - "\n", - " [[ 0., 0.],\n", - " [ 0., -1.]]], dtype=torch.float64), tensor([[[ 0.5000],\n", - " [ 0.0000]],\n", - "\n", - " [[ 0.0000],\n", - " [-0.5000]]], dtype=torch.float64)]\n", - "Mode size [2, 2, 2]\n", - "TT rank [1, 2, 2, 1]\n" - ] - } - ], "source": [ "import torchtt as tntt\n", "full_tensor = result.reshape(2, 2, 2)\n", @@ -223,29 +173,13 @@ "print('TT cores', tens_tt.cores)\n", "print('Mode size ', tens_tt.N)\n", "print('TT rank ', tens_tt.R)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original Matrix:\n", - " tensor([[0.0000, 0.5000],\n", - " [0.5000, 0.0000]], dtype=torch.float64)\n", - "Q Matrix:\n", - " tensor([[ 0., -1.],\n", - " [-1., 0.]], dtype=torch.float64)\n", - "R Matrix:\n", - " tensor([[-0.5000, 0.0000],\n", - " [ 0.0000, -0.5000]], dtype=torch.float64)\n" - ] - } - ], "source": [ "# Reshape the tensor into a 2x2 matrix\n", "matrix = result.reshape(2, 2)\n", @@ -257,24 +191,13 @@ "print(\"Original Matrix:\\n\", matrix)\n", "print(\"Q Matrix:\\n\", q)\n", "print(\"R Matrix:\\n\", r)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -301,24 +224,13 @@ "basis_functions = linear_FEM_basis(x_plot, n)\n", "basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -346,24 +258,13 @@ "basis_functions = linear_FEM_basis(x_plot, n)\n", "basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -395,26 +296,13 @@ "basis_functions = linear_FEM_basis(x_plot, n)\n", "basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'float' object has no attribute 'shape'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/mazen/Research/QC/QuLearn/scratch/scratch2.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 10\u001b[0m \u001b[39mreturn\u001b[39;00m qml\u001b[39m.\u001b[39mexpval(qml\u001b[39m.\u001b[39mPauliZ(\u001b[39m0\u001b[39m)) \u001b[39m# qml.state()\u001b[39;00m\n\u001b[1;32m 13\u001b[0m x \u001b[39m=\u001b[39m \u001b[39m0.3\u001b[39m\n\u001b[0;32m---> 14\u001b[0m f \u001b[39m=\u001b[39m linear_FEM_basis(x, num_qubits)\n\u001b[1;32m 15\u001b[0m f \u001b[39m=\u001b[39m f \u001b[39m/\u001b[39m np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm(f)\n\u001b[1;32m 16\u001b[0m \u001b[39mprint\u001b[39m(f)\n", - "\u001b[1;32m/home/mazen/Research/QC/QuLearn/scratch/scratch2.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 7\u001b[0m nodes \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtensor(nodes, dtype\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mfloat64)\n\u001b[1;32m 9\u001b[0m \u001b[39m# Initialize the output tensor\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m values \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mzeros(x\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39m], num_nodes, dtype\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mfloat64)\n\u001b[1;32m 12\u001b[0m \u001b[39m# Distance between nodes\u001b[39;00m\n\u001b[1;32m 13\u001b[0m h \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m \u001b[39m/\u001b[39m (num_nodes \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'shape'" - ] - } - ], "source": [ "import pennylane as qml\n", "\n", @@ -432,13 +320,13 @@ "f = linear_FEM_basis(x, num_qubits)\n", "f = f / np.linalg.norm(f)\n", "print(f)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -455,23 +343,23 @@ " #Phi = sawtooth_vector(x, self.num_qubits)\n", " Phi = Phi / torch.linalg.norm(Phi)\n", " qml.AmplitudeEmbedding(features=Phi, wires=self.wires, normalize=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, - "outputs": [], "source": [ "num_qubits = 3\n", "var0 = qlayer.AltRotCXLayer(wires=num_qubits, n_layers=3)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -643,23 +531,13 @@ " self.wires[0],\n", " )\n", "\n" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0: ─╭QubitStateVector(M0)──Rot(4.83,5.52,0.12)─┤ ╭<𝓗>\n", - "1: ─├QubitStateVector(M0)──Rot(2.38,0.68,4.64)─┤ ├<𝓗>\n", - "2: ─╰QubitStateVector(M0)──Rot(3.80,1.52,4.28)─┤ ╰<𝓗>\n" - ] - } - ], "source": [ "from qulearn import qlayer\n", "import pennylane as qml\n", @@ -741,29 +619,13 @@ "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", "x = torch.rand((1), dtype=torch.float64)\n", "print(drawer(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[ 0.1185+0.0000j, 0.0936+0.2561j, -0.0816+0.1230j, 0.0195+0.0892j],\n", - " [ 0.0936-0.2561j, 0.6273+0.0000j, 0.2012+0.2735j, 0.2082+0.0283j],\n", - " [-0.0816-0.1230j, 0.2012-0.2735j, 0.1837+0.0000j, 0.0791-0.0817j],\n", - " [ 0.0195-0.0892j, 0.2082-0.0283j, 0.0791+0.0817j, 0.0704+0.0000j]],\n", - " grad_fn=)\n", - "tensor([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j]])\n" - ] - } - ], "source": [ "import pennylane as qml\n", "import torch\n", @@ -774,13 +636,13 @@ "res = torch.mm(torch.mm(U().conj().t(), O), U())\n", "print(res)\n", "print(O)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -806,24 +668,13 @@ "print(\"IIX:\\n\", IIX)\n", "print(\"IIY:\\n\", IIY)\n", "print(\"YII:\\n\", YII)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -857,13 +708,13 @@ "plt.legend()\n", "plt.grid(True)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "a = 0.3\n", "b = 0.5\n", @@ -891,13 +742,13 @@ " beta = beta1 * (lam1 + 1) * (1 + a - b) + beta2 * (b - a) * (lam2 + 1)\n", " # beta = np.cos(beta1) + np.sin(beta2)\n", " return beta" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -935,13 +786,13 @@ "ax.set_zlabel(\"effbeta\")\n", "ax.set_title(\"Surface Plot of effbeta\")\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from qulearn.qlayer import CircuitLayer\n", "import pennylane as qml\n", @@ -962,20 +813,20 @@ " def circuit(self, x):\n", " for xj, w in zip(x, self.wires):\n", " qml.RY(xj, w)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "source": [], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn.qlayer import (\n", @@ -1033,13 +884,13 @@ "print(\"x =\", x.shape)\n", "y = model(x)\n", "print(\"y =\", y.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -1124,13 +975,13 @@ " # Update the parameters with the new coefficients\n", " self.alpha.data = coefficients[: self.num_omegas]\n", " self.beta.data = coefficients[self.num_omegas :]" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -1217,13 +1068,13 @@ "output = model(x)\n", "print(output)\n", "print(output.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torch import nn\n", "import torch.nn.functional as F\n", @@ -1430,13 +1281,13 @@ "# tmp = torch.mm(y.t(), y)\n", "# print(tmp)\n", "# tmp.backward()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "x = torch.randn((num_features), dtype=torch.float64)\n", "print(x.shape)\n", @@ -1444,13 +1295,13 @@ "print(x.shape)\n", "x = x.squeeze(-1)\n", "print(x.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import torch.optim as optim\n", @@ -1510,13 +1361,13 @@ "\n", " if step % 10 == 0:\n", " print(f\"Step {step}, Loss: {loss.item()}\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -1565,13 +1416,13 @@ " plt.legend()\n", " plt.grid(True)\n", " plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import torch.optim as optim\n", @@ -1599,13 +1450,13 @@ "Y_train = torch.tensor(Y_train, dtype=torch.float64).view(-1, 1)\n", "X_valid = torch.tensor(X_valid, dtype=torch.float64)\n", "Y_valid = torch.tensor(Y_valid, dtype=torch.float64).view(-1, 1)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "from pyDOE import lhs\n", @@ -1682,13 +1533,13 @@ "Y_train = torch.tensor(Y_train, dtype=torch.float64).view(-1, 1)\n", "X_valid = torch.tensor(X_valid, dtype=torch.float64)\n", "Y_valid = torch.tensor(Y_valid, dtype=torch.float64).view(-1, 1)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "\n", @@ -1752,13 +1603,13 @@ "Y_train = torch.tensor(Y_train, dtype=torch.float64).view(-1, 1)\n", "X_valid = torch.tensor(X_valid, dtype=torch.float64)\n", "Y_valid = torch.tensor(Y_valid, dtype=torch.float64).view(-1, 1)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -1771,13 +1622,13 @@ "X_valid = torch.tensor(X_valid, dtype=torch.float64)\n", "Y_train = torch.tensor(Y_train, dtype=torch.float64).unsqueeze(1)\n", "Y_valid = torch.tensor(Y_valid, dtype=torch.float64).unsqueeze(1)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 178, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -1916,24 +1767,13 @@ "\n", "\n", "Y_high_low = add_gaussian_noise(high_low(X))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -1946,13 +1786,13 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -1972,13 +1812,13 @@ "data_valid = TensorDataset(X_valid, Y_valid)\n", "loader_train = DataLoader(data_train, batch_size=batch_size, shuffle=False)\n", "loader_valid = DataLoader(data_valid, batch_size=batch_size, shuffle=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "model = relu\n", "y = model(X_train)\n", @@ -1992,13 +1832,13 @@ "model = hybrid\n", "y = model(X_train)\n", "print(y.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -2012,13 +1852,13 @@ "data_valid = TensorDataset(X_valid, Y_valid)\n", "loader_train = DataLoader(data_train, batch_size=batch_size, shuffle=True)\n", "loader_valid = DataLoader(data_valid, batch_size=batch_size, shuffle=True)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -2033,13 +1873,13 @@ "optimizer = Adam(model.parameters(), lr=lr, amsgrad=True)\n", "loss_fn = torch.nn.MSELoss()\n", "metric = MeanAbsolutePercentageError()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, - "outputs": [], "source": [ "logger = logging.getLogger(\"train_function\")\n", "logger.setLevel(level=logging.INFO)\n", @@ -2051,441 +1891,23 @@ " num_epochs=num_epochs,\n", " logger=logger,\n", ")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:train_function:Train - Epoch: 1, Loss: 0.748526, Metrics: MARE: 0.779388\n", - "INFO:train_function:Validate - Epoch: 1, Loss: 0.471923, Metrics: MARE: 0.732617\n", - "INFO:train_function:Train - Epoch: 2, Loss: 0.330638, Metrics: MARE: 0.500763\n", - "INFO:train_function:Validate - Epoch: 2, Loss: 0.199134, Metrics: MARE: 0.512214\n", - "INFO:train_function:Train - Epoch: 3, Loss: 0.108261, Metrics: MARE: 0.311547\n", - "INFO:train_function:Validate - Epoch: 3, Loss: 0.068448, Metrics: MARE: 0.300680\n", - "INFO:train_function:Train - Epoch: 4, Loss: 0.075707, Metrics: MARE: 0.248871\n", - "INFO:train_function:Validate - Epoch: 4, Loss: 0.059307, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 5, Loss: 0.107496, Metrics: MARE: 0.256872\n", - "INFO:train_function:Validate - Epoch: 5, Loss: 0.084355, Metrics: MARE: 0.254385\n", - "INFO:train_function:Train - Epoch: 6, Loss: 0.108931, Metrics: MARE: 0.272904\n", - "INFO:train_function:Validate - Epoch: 6, Loss: 0.087002, Metrics: MARE: 0.279545\n", - "INFO:train_function:Train - Epoch: 7, Loss: 0.077698, Metrics: MARE: 0.244016\n", - "INFO:train_function:Validate - Epoch: 7, Loss: 0.068788, Metrics: MARE: 0.239149\n", - "INFO:train_function:Train - Epoch: 8, Loss: 0.050440, Metrics: MARE: 0.222615\n", - "INFO:train_function:Validate - Epoch: 8, Loss: 0.052167, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 9, Loss: 0.045671, Metrics: MARE: 0.218862\n", - "INFO:train_function:Validate - Epoch: 9, Loss: 0.048768, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 10, Loss: 0.052218, Metrics: MARE: 0.230028\n", - "INFO:train_function:Validate - Epoch: 10, Loss: 0.052631, Metrics: MARE: 0.210205\n", - "INFO:train_function:Train - Epoch: 11, Loss: 0.054264, Metrics: MARE: 0.233039\n", - "INFO:train_function:Validate - Epoch: 11, Loss: 0.054121, Metrics: MARE: 0.206753\n", - "INFO:train_function:Train - Epoch: 12, Loss: 0.049888, Metrics: MARE: 0.226327\n", - "INFO:train_function:Validate - Epoch: 12, Loss: 0.051560, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 13, Loss: 0.045684, Metrics: MARE: 0.219105\n", - "INFO:train_function:Validate - Epoch: 13, Loss: 0.048955, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 14, Loss: 0.044942, Metrics: MARE: 0.217761\n", - "INFO:train_function:Validate - Epoch: 14, Loss: 0.048586, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 15, Loss: 0.045538, Metrics: MARE: 0.217519\n", - "INFO:train_function:Validate - Epoch: 15, Loss: 0.049113, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 16, Loss: 0.045271, Metrics: MARE: 0.216639\n", - "INFO:train_function:Validate - Epoch: 16, Loss: 0.049019, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 17, Loss: 0.044523, Metrics: MARE: 0.216220\n", - "INFO:train_function:Validate - Epoch: 17, Loss: 0.048566, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 18, Loss: 0.044292, Metrics: MARE: 0.215994\n", - "INFO:train_function:Validate - Epoch: 18, Loss: 0.048449, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 19, Loss: 0.044489, Metrics: MARE: 0.215875\n", - "INFO:train_function:Validate - Epoch: 19, Loss: 0.048592, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 20, Loss: 0.044560, Metrics: MARE: 0.215844\n", - "INFO:train_function:Validate - Epoch: 20, Loss: 0.048626, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 21, Loss: 0.044418, Metrics: MARE: 0.215877\n", - "INFO:train_function:Validate - Epoch: 21, Loss: 0.048521, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 22, Loss: 0.044297, Metrics: MARE: 0.215951\n", - "INFO:train_function:Validate - Epoch: 22, Loss: 0.048445, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 23, Loss: 0.044281, Metrics: MARE: 0.216047\n", - "INFO:train_function:Validate - Epoch: 23, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 24, Loss: 0.044289, Metrics: MARE: 0.216144\n", - "INFO:train_function:Validate - Epoch: 24, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 25, Loss: 0.044283, Metrics: MARE: 0.216229\n", - "INFO:train_function:Validate - Epoch: 25, Loss: 0.048429, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 26, Loss: 0.044288, Metrics: MARE: 0.216291\n", - "INFO:train_function:Validate - Epoch: 26, Loss: 0.048432, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 27, Loss: 0.044307, Metrics: MARE: 0.216325\n", - "INFO:train_function:Validate - Epoch: 27, Loss: 0.048445, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 28, Loss: 0.044316, Metrics: MARE: 0.216334\n", - "INFO:train_function:Validate - Epoch: 28, Loss: 0.048451, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 29, Loss: 0.044308, Metrics: MARE: 0.216325\n", - "INFO:train_function:Validate - Epoch: 29, Loss: 0.048446, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 30, Loss: 0.044295, Metrics: MARE: 0.216305\n", - "INFO:train_function:Validate - Epoch: 30, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 31, Loss: 0.044288, Metrics: MARE: 0.216283\n", - "INFO:train_function:Validate - Epoch: 31, Loss: 0.048433, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 32, Loss: 0.044286, Metrics: MARE: 0.216265\n", - "INFO:train_function:Validate - Epoch: 32, Loss: 0.048432, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 33, Loss: 0.044286, Metrics: MARE: 0.216253\n", - "INFO:train_function:Validate - Epoch: 33, Loss: 0.048433, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 34, Loss: 0.044288, Metrics: MARE: 0.216248\n", - "INFO:train_function:Validate - Epoch: 34, Loss: 0.048435, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 35, Loss: 0.044291, Metrics: MARE: 0.216249\n", - "INFO:train_function:Validate - Epoch: 35, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 36, Loss: 0.044292, Metrics: MARE: 0.216253\n", - "INFO:train_function:Validate - Epoch: 36, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 37, Loss: 0.044292, Metrics: MARE: 0.216259\n", - "INFO:train_function:Validate - Epoch: 37, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 38, Loss: 0.044291, Metrics: MARE: 0.216266\n", - "INFO:train_function:Validate - Epoch: 38, Loss: 0.048436, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 39, Loss: 0.044290, Metrics: MARE: 0.216273\n", - "INFO:train_function:Validate - Epoch: 39, Loss: 0.048435, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 40, Loss: 0.044291, Metrics: MARE: 0.216278\n", - "INFO:train_function:Validate - Epoch: 40, Loss: 0.048435, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 41, Loss: 0.044292, Metrics: MARE: 0.216282\n", - "INFO:train_function:Validate - Epoch: 41, Loss: 0.048436, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 42, Loss: 0.044293, Metrics: MARE: 0.216285\n", - "INFO:train_function:Validate - Epoch: 42, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 43, Loss: 0.044294, Metrics: MARE: 0.216286\n", - "INFO:train_function:Validate - Epoch: 43, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 44, Loss: 0.044294, Metrics: MARE: 0.216286\n", - "INFO:train_function:Validate - Epoch: 44, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 45, Loss: 0.044294, Metrics: MARE: 0.216287\n", - "INFO:train_function:Validate - Epoch: 45, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 46, Loss: 0.044293, Metrics: MARE: 0.216287\n", - "INFO:train_function:Validate - Epoch: 46, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 47, Loss: 0.044293, Metrics: MARE: 0.216288\n", - "INFO:train_function:Validate - Epoch: 47, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 48, Loss: 0.044294, Metrics: MARE: 0.216289\n", - "INFO:train_function:Validate - Epoch: 48, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 49, Loss: 0.044294, Metrics: MARE: 0.216291\n", - "INFO:train_function:Validate - Epoch: 49, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 50, Loss: 0.044294, Metrics: MARE: 0.216292\n", - "INFO:train_function:Validate - Epoch: 50, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 51, Loss: 0.044295, Metrics: MARE: 0.216294\n", - "INFO:train_function:Validate - Epoch: 51, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 52, Loss: 0.044295, Metrics: MARE: 0.216296\n", - "INFO:train_function:Validate - Epoch: 52, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 53, Loss: 0.044295, Metrics: MARE: 0.216298\n", - "INFO:train_function:Validate - Epoch: 53, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 54, Loss: 0.044295, Metrics: MARE: 0.216299\n", - "INFO:train_function:Validate - Epoch: 54, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 55, Loss: 0.044295, Metrics: MARE: 0.216300\n", - "INFO:train_function:Validate - Epoch: 55, Loss: 0.048437, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 56, Loss: 0.044296, Metrics: MARE: 0.216302\n", - "INFO:train_function:Validate - Epoch: 56, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 57, Loss: 0.044296, Metrics: MARE: 0.216303\n", - "INFO:train_function:Validate - Epoch: 57, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 58, Loss: 0.044296, Metrics: MARE: 0.216304\n", - "INFO:train_function:Validate - Epoch: 58, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 59, Loss: 0.044296, Metrics: MARE: 0.216305\n", - "INFO:train_function:Validate - Epoch: 59, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 60, Loss: 0.044296, Metrics: MARE: 0.216306\n", - "INFO:train_function:Validate - Epoch: 60, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 61, Loss: 0.044297, Metrics: MARE: 0.216308\n", - "INFO:train_function:Validate - Epoch: 61, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 62, Loss: 0.044297, Metrics: MARE: 0.216309\n", - "INFO:train_function:Validate - Epoch: 62, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 63, Loss: 0.044297, Metrics: MARE: 0.216310\n", - "INFO:train_function:Validate - Epoch: 63, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 64, Loss: 0.044297, Metrics: MARE: 0.216311\n", - "INFO:train_function:Validate - Epoch: 64, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 65, Loss: 0.044297, Metrics: MARE: 0.216312\n", - "INFO:train_function:Validate - Epoch: 65, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 66, Loss: 0.044298, Metrics: MARE: 0.216313\n", - "INFO:train_function:Validate - Epoch: 66, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 67, Loss: 0.044298, Metrics: MARE: 0.216314\n", - "INFO:train_function:Validate - Epoch: 67, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 68, Loss: 0.044298, Metrics: MARE: 0.216315\n", - "INFO:train_function:Validate - Epoch: 68, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 69, Loss: 0.044298, Metrics: MARE: 0.216316\n", - "INFO:train_function:Validate - Epoch: 69, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 70, Loss: 0.044298, Metrics: MARE: 0.216317\n", - "INFO:train_function:Validate - Epoch: 70, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 71, Loss: 0.044298, Metrics: MARE: 0.216318\n", - "INFO:train_function:Validate - Epoch: 71, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 72, Loss: 0.044299, Metrics: MARE: 0.216319\n", - "INFO:train_function:Validate - Epoch: 72, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 73, Loss: 0.044299, Metrics: MARE: 0.216320\n", - "INFO:train_function:Validate - Epoch: 73, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 74, Loss: 0.044299, Metrics: MARE: 0.216321\n", - "INFO:train_function:Validate - Epoch: 74, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 75, Loss: 0.044299, Metrics: MARE: 0.216322\n", - "INFO:train_function:Validate - Epoch: 75, Loss: 0.048438, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 76, Loss: 0.044299, Metrics: MARE: 0.216323\n", - "INFO:train_function:Validate - Epoch: 76, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 77, Loss: 0.044299, Metrics: MARE: 0.216324\n", - "INFO:train_function:Validate - Epoch: 77, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 78, Loss: 0.044299, Metrics: MARE: 0.216325\n", - "INFO:train_function:Validate - Epoch: 78, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 79, Loss: 0.044300, Metrics: MARE: 0.216325\n", - "INFO:train_function:Validate - Epoch: 79, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 80, Loss: 0.044300, Metrics: MARE: 0.216326\n", - "INFO:train_function:Validate - Epoch: 80, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 81, Loss: 0.044300, Metrics: MARE: 0.216327\n", - "INFO:train_function:Validate - Epoch: 81, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 82, Loss: 0.044300, Metrics: MARE: 0.216328\n", - "INFO:train_function:Validate - Epoch: 82, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 83, Loss: 0.044300, Metrics: MARE: 0.216329\n", - "INFO:train_function:Validate - Epoch: 83, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 84, Loss: 0.044300, Metrics: MARE: 0.216329\n", - "INFO:train_function:Validate - Epoch: 84, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 85, Loss: 0.044300, Metrics: MARE: 0.216330\n", - "INFO:train_function:Validate - Epoch: 85, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 86, Loss: 0.044300, Metrics: MARE: 0.216331\n", - "INFO:train_function:Validate - Epoch: 86, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 87, Loss: 0.044301, Metrics: MARE: 0.216332\n", - "INFO:train_function:Validate - Epoch: 87, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 88, Loss: 0.044301, Metrics: MARE: 0.216332\n", - "INFO:train_function:Validate - Epoch: 88, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 89, Loss: 0.044301, Metrics: MARE: 0.216333\n", - "INFO:train_function:Validate - Epoch: 89, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 90, Loss: 0.044301, Metrics: MARE: 0.216334\n", - "INFO:train_function:Validate - Epoch: 90, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 91, Loss: 0.044301, Metrics: MARE: 0.216335\n", - "INFO:train_function:Validate - Epoch: 91, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 92, Loss: 0.044301, Metrics: MARE: 0.216335\n", - "INFO:train_function:Validate - Epoch: 92, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 93, Loss: 0.044301, Metrics: MARE: 0.216336\n", - "INFO:train_function:Validate - Epoch: 93, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 94, Loss: 0.044301, Metrics: MARE: 0.216337\n", - "INFO:train_function:Validate - Epoch: 94, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 95, Loss: 0.044302, Metrics: MARE: 0.216337\n", - "INFO:train_function:Validate - Epoch: 95, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 96, Loss: 0.044302, Metrics: MARE: 0.216338\n", - "INFO:train_function:Validate - Epoch: 96, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 97, Loss: 0.044302, Metrics: MARE: 0.216339\n", - "INFO:train_function:Validate - Epoch: 97, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 98, Loss: 0.044302, Metrics: MARE: 0.216339\n", - "INFO:train_function:Validate - Epoch: 98, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 99, Loss: 0.044302, Metrics: MARE: 0.216340\n", - "INFO:train_function:Validate - Epoch: 99, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 100, Loss: 0.044302, Metrics: MARE: 0.216340\n", - "INFO:train_function:Validate - Epoch: 100, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 101, Loss: 0.044302, Metrics: MARE: 0.216341\n", - "INFO:train_function:Validate - Epoch: 101, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 102, Loss: 0.044302, Metrics: MARE: 0.216342\n", - "INFO:train_function:Validate - Epoch: 102, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 103, Loss: 0.044302, Metrics: MARE: 0.216342\n", - "INFO:train_function:Validate - Epoch: 103, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 104, Loss: 0.044303, Metrics: MARE: 0.216343\n", - "INFO:train_function:Validate - Epoch: 104, Loss: 0.048439, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 105, Loss: 0.044303, Metrics: MARE: 0.216343\n", - "INFO:train_function:Validate - Epoch: 105, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 106, Loss: 0.044303, Metrics: MARE: 0.216344\n", - "INFO:train_function:Validate - Epoch: 106, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 107, Loss: 0.044303, Metrics: MARE: 0.216345\n", - "INFO:train_function:Validate - Epoch: 107, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 108, Loss: 0.044303, Metrics: MARE: 0.216345\n", - "INFO:train_function:Validate - Epoch: 108, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 109, Loss: 0.044303, Metrics: MARE: 0.216346\n", - "INFO:train_function:Validate - Epoch: 109, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 110, Loss: 0.044303, Metrics: MARE: 0.216346\n", - "INFO:train_function:Validate - Epoch: 110, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 111, Loss: 0.044303, Metrics: MARE: 0.216347\n", - "INFO:train_function:Validate - Epoch: 111, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 112, Loss: 0.044303, Metrics: MARE: 0.216347\n", - "INFO:train_function:Validate - Epoch: 112, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 113, Loss: 0.044303, Metrics: MARE: 0.216348\n", - "INFO:train_function:Validate - Epoch: 113, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 114, Loss: 0.044303, Metrics: MARE: 0.216348\n", - "INFO:train_function:Validate - Epoch: 114, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 115, Loss: 0.044304, Metrics: MARE: 0.216349\n", - "INFO:train_function:Validate - Epoch: 115, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 116, Loss: 0.044304, Metrics: MARE: 0.216349\n", - "INFO:train_function:Validate - Epoch: 116, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 117, Loss: 0.044304, Metrics: MARE: 0.216350\n", - "INFO:train_function:Validate - Epoch: 117, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 118, Loss: 0.044304, Metrics: MARE: 0.216350\n", - "INFO:train_function:Validate - Epoch: 118, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 119, Loss: 0.044304, Metrics: MARE: 0.216351\n", - "INFO:train_function:Validate - Epoch: 119, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 120, Loss: 0.044304, Metrics: MARE: 0.216351\n", - "INFO:train_function:Validate - Epoch: 120, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 121, Loss: 0.044304, Metrics: MARE: 0.216352\n", - "INFO:train_function:Validate - Epoch: 121, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 122, Loss: 0.044304, Metrics: MARE: 0.216352\n", - "INFO:train_function:Validate - Epoch: 122, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 123, Loss: 0.044304, Metrics: MARE: 0.216352\n", - "INFO:train_function:Validate - Epoch: 123, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 124, Loss: 0.044304, Metrics: MARE: 0.216353\n", - "INFO:train_function:Validate - Epoch: 124, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 125, Loss: 0.044304, Metrics: MARE: 0.216353\n", - "INFO:train_function:Validate - Epoch: 125, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 126, Loss: 0.044304, Metrics: MARE: 0.216354\n", - "INFO:train_function:Validate - Epoch: 126, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 127, Loss: 0.044305, Metrics: MARE: 0.216354\n", - "INFO:train_function:Validate - Epoch: 127, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 128, Loss: 0.044305, Metrics: MARE: 0.216355\n", - "INFO:train_function:Validate - Epoch: 128, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 129, Loss: 0.044305, Metrics: MARE: 0.216355\n", - "INFO:train_function:Validate - Epoch: 129, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 130, Loss: 0.044305, Metrics: MARE: 0.216356\n", - "INFO:train_function:Validate - Epoch: 130, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 131, Loss: 0.044305, Metrics: MARE: 0.216356\n", - "INFO:train_function:Validate - Epoch: 131, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 132, Loss: 0.044305, Metrics: MARE: 0.216356\n", - "INFO:train_function:Validate - Epoch: 132, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 133, Loss: 0.044305, Metrics: MARE: 0.216357\n", - "INFO:train_function:Validate - Epoch: 133, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 134, Loss: 0.044305, Metrics: MARE: 0.216357\n", - "INFO:train_function:Validate - Epoch: 134, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 135, Loss: 0.044305, Metrics: MARE: 0.216358\n", - "INFO:train_function:Validate - Epoch: 135, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 136, Loss: 0.044305, Metrics: MARE: 0.216358\n", - "INFO:train_function:Validate - Epoch: 136, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 137, Loss: 0.044305, Metrics: MARE: 0.216358\n", - "INFO:train_function:Validate - Epoch: 137, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 138, Loss: 0.044305, Metrics: MARE: 0.216359\n", - "INFO:train_function:Validate - Epoch: 138, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 139, Loss: 0.044305, Metrics: MARE: 0.216359\n", - "INFO:train_function:Validate - Epoch: 139, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 140, Loss: 0.044305, Metrics: MARE: 0.216359\n", - "INFO:train_function:Validate - Epoch: 140, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 141, Loss: 0.044306, Metrics: MARE: 0.216360\n", - "INFO:train_function:Validate - Epoch: 141, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 142, Loss: 0.044306, Metrics: MARE: 0.216360\n", - "INFO:train_function:Validate - Epoch: 142, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 143, Loss: 0.044306, Metrics: MARE: 0.216361\n", - "INFO:train_function:Validate - Epoch: 143, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 144, Loss: 0.044306, Metrics: MARE: 0.216361\n", - "INFO:train_function:Validate - Epoch: 144, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 145, Loss: 0.044306, Metrics: MARE: 0.216361\n", - "INFO:train_function:Validate - Epoch: 145, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 146, Loss: 0.044306, Metrics: MARE: 0.216362\n", - "INFO:train_function:Validate - Epoch: 146, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 147, Loss: 0.044306, Metrics: MARE: 0.216362\n", - "INFO:train_function:Validate - Epoch: 147, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 148, Loss: 0.044306, Metrics: MARE: 0.216362\n", - "INFO:train_function:Validate - Epoch: 148, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 149, Loss: 0.044306, Metrics: MARE: 0.216363\n", - "INFO:train_function:Validate - Epoch: 149, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 150, Loss: 0.044306, Metrics: MARE: 0.216363\n", - "INFO:train_function:Validate - Epoch: 150, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 151, Loss: 0.044306, Metrics: MARE: 0.216363\n", - "INFO:train_function:Validate - Epoch: 151, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 152, Loss: 0.044306, Metrics: MARE: 0.216364\n", - "INFO:train_function:Validate - Epoch: 152, Loss: 0.048440, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 153, Loss: 0.044306, Metrics: MARE: 0.216364\n", - "INFO:train_function:Validate - Epoch: 153, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 154, Loss: 0.044306, Metrics: MARE: 0.216364\n", - "INFO:train_function:Validate - Epoch: 154, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 155, Loss: 0.044306, Metrics: MARE: 0.216365\n", - "INFO:train_function:Validate - Epoch: 155, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 156, Loss: 0.044306, Metrics: MARE: 0.216365\n", - "INFO:train_function:Validate - Epoch: 156, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 157, Loss: 0.044306, Metrics: MARE: 0.216365\n", - "INFO:train_function:Validate - Epoch: 157, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 158, Loss: 0.044307, Metrics: MARE: 0.216365\n", - "INFO:train_function:Validate - Epoch: 158, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 159, Loss: 0.044307, Metrics: MARE: 0.216366\n", - "INFO:train_function:Validate - Epoch: 159, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 160, Loss: 0.044307, Metrics: MARE: 0.216366\n", - "INFO:train_function:Validate - Epoch: 160, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 161, Loss: 0.044307, Metrics: MARE: 0.216366\n", - "INFO:train_function:Validate - Epoch: 161, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 162, Loss: 0.044307, Metrics: MARE: 0.216367\n", - "INFO:train_function:Validate - Epoch: 162, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 163, Loss: 0.044307, Metrics: MARE: 0.216367\n", - "INFO:train_function:Validate - Epoch: 163, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 164, Loss: 0.044307, Metrics: MARE: 0.216367\n", - "INFO:train_function:Validate - Epoch: 164, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 165, Loss: 0.044307, Metrics: MARE: 0.216368\n", - "INFO:train_function:Validate - Epoch: 165, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 166, Loss: 0.044307, Metrics: MARE: 0.216368\n", - "INFO:train_function:Validate - Epoch: 166, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 167, Loss: 0.044307, Metrics: MARE: 0.216368\n", - "INFO:train_function:Validate - Epoch: 167, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 168, Loss: 0.044307, Metrics: MARE: 0.216368\n", - "INFO:train_function:Validate - Epoch: 168, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 169, Loss: 0.044307, Metrics: MARE: 0.216369\n", - "INFO:train_function:Validate - Epoch: 169, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 170, Loss: 0.044307, Metrics: MARE: 0.216369\n", - "INFO:train_function:Validate - Epoch: 170, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 171, Loss: 0.044307, Metrics: MARE: 0.216369\n", - "INFO:train_function:Validate - Epoch: 171, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 172, Loss: 0.044307, Metrics: MARE: 0.216369\n", - "INFO:train_function:Validate - Epoch: 172, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 173, Loss: 0.044307, Metrics: MARE: 0.216370\n", - "INFO:train_function:Validate - Epoch: 173, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 174, Loss: 0.044307, Metrics: MARE: 0.216370\n", - "INFO:train_function:Validate - Epoch: 174, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 175, Loss: 0.044307, Metrics: MARE: 0.216370\n", - "INFO:train_function:Validate - Epoch: 175, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 176, Loss: 0.044307, Metrics: MARE: 0.216371\n", - "INFO:train_function:Validate - Epoch: 176, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 177, Loss: 0.044308, Metrics: MARE: 0.216371\n", - "INFO:train_function:Validate - Epoch: 177, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 178, Loss: 0.044308, Metrics: MARE: 0.216371\n", - "INFO:train_function:Validate - Epoch: 178, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 179, Loss: 0.044308, Metrics: MARE: 0.216371\n", - "INFO:train_function:Validate - Epoch: 179, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 180, Loss: 0.044308, Metrics: MARE: 0.216372\n", - "INFO:train_function:Validate - Epoch: 180, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 181, Loss: 0.044308, Metrics: MARE: 0.216372\n", - "INFO:train_function:Validate - Epoch: 181, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 182, Loss: 0.044308, Metrics: MARE: 0.216372\n", - "INFO:train_function:Validate - Epoch: 182, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 183, Loss: 0.044308, Metrics: MARE: 0.216372\n", - "INFO:train_function:Validate - Epoch: 183, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 184, Loss: 0.044308, Metrics: MARE: 0.216372\n", - "INFO:train_function:Validate - Epoch: 184, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 185, Loss: 0.044308, Metrics: MARE: 0.216373\n", - "INFO:train_function:Validate - Epoch: 185, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 186, Loss: 0.044308, Metrics: MARE: 0.216373\n", - "INFO:train_function:Validate - Epoch: 186, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 187, Loss: 0.044308, Metrics: MARE: 0.216373\n", - "INFO:train_function:Validate - Epoch: 187, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 188, Loss: 0.044308, Metrics: MARE: 0.216373\n", - "INFO:train_function:Validate - Epoch: 188, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 189, Loss: 0.044308, Metrics: MARE: 0.216374\n", - "INFO:train_function:Validate - Epoch: 189, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 190, Loss: 0.044308, Metrics: MARE: 0.216374\n", - "INFO:train_function:Validate - Epoch: 190, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 191, Loss: 0.044308, Metrics: MARE: 0.216374\n", - "INFO:train_function:Validate - Epoch: 191, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 192, Loss: 0.044308, Metrics: MARE: 0.216374\n", - "INFO:train_function:Validate - Epoch: 192, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 193, Loss: 0.044308, Metrics: MARE: 0.216375\n", - "INFO:train_function:Validate - Epoch: 193, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 194, Loss: 0.044308, Metrics: MARE: 0.216375\n", - "INFO:train_function:Validate - Epoch: 194, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 195, Loss: 0.044308, Metrics: MARE: 0.216375\n", - "INFO:train_function:Validate - Epoch: 195, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 196, Loss: 0.044308, Metrics: MARE: 0.216375\n", - "INFO:train_function:Validate - Epoch: 196, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 197, Loss: 0.044308, Metrics: MARE: 0.216375\n", - "INFO:train_function:Validate - Epoch: 197, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 198, Loss: 0.044308, Metrics: MARE: 0.216376\n", - "INFO:train_function:Validate - Epoch: 198, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 199, Loss: 0.044308, Metrics: MARE: 0.216376\n", - "INFO:train_function:Validate - Epoch: 199, Loss: 0.048441, Metrics: MARE: 0.204016\n", - "INFO:train_function:Train - Epoch: 200, Loss: 0.044308, Metrics: MARE: 0.216376\n", - "INFO:train_function:Validate - Epoch: 200, Loss: 0.048441, Metrics: MARE: 0.204016\n" - ] - } - ], "source": [ "# Train\n", "trainer.train(model, train_data=loader_train, valid_data=loader_valid)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Plotting\n", "X = torch.linspace(-1, 1, 1000, dtype=torch.float64).reshape(-1, 1)\n", @@ -2507,13 +1929,13 @@ "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torchmetrics.regression import MeanAbsolutePercentageError\n", "\n", @@ -2525,24 +1947,24 @@ "loss_valid = metric(predicted_valid, Y_valid)\n", "print(\"train loss: \", loss_train)\n", "print(\"valid_loss: \", loss_valid)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from torchmetrics.regression import MeanAbsolutePercentageError\n", "\n", "model.fit_fourier_coefficients(X_train, Y_train)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "predicted_train = model(X_train)\n", "predicted_valid = model(X_valid)\n", @@ -2553,7 +1975,8 @@ "loss_valid = metric(predicted_valid, Y_valid)\n", "print(\"train loss: \", loss_train)\n", "print(\"valid_loss: \", loss_valid)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -2598,7 +2021,6 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2634,13 +2056,13 @@ "ax.set_zlabel(\"effbeta\")\n", "ax.set_title(\"Surface Plot of effbeta\")\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2675,7 +2097,8 @@ "ax.set_zlabel(\"effbeta\")\n", "ax.set_title(\"Surface Plot of effbeta\")\n", "plt.show()" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/scratch/scratch3.ipynb b/scratch/scratch3.ipynb index 4e3cb8b..d9198e1 100644 --- a/scratch/scratch3.ipynb +++ b/scratch/scratch3.ipynb @@ -4,17 +4,6 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Left Points: tensor([-1.0000, -1.0000, -0.1429, -0.4286, 0.7143, 0.7143])\n", - "Right Points: tensor([-0.7143, -0.7143, 0.1429, -0.1429, 1.0000, 1.0000])\n", - "Position: tensor([0., 0., 3., 2., 6., 6.])\n" - ] - } - ], "source": [ "import torch\n", "\n", @@ -46,23 +35,13 @@ "print(\"Left Points:\", left_points)\n", "print(\"Right Points:\", right_points)\n", "print(\"Position:\", position)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.8660, 0.5000, 0.5000, 0.8660, 0.8660, 0.5000, 0.8660, 0.5000]],\n", - " dtype=torch.float64)\n", - "torch.Size([1, 8])\n" - ] - } - ], "source": [ "import torch\n", "\n", @@ -111,28 +90,13 @@ "n = 3\n", "print(sawtooth_vector(x, n))\n", "print(sawtooth_vector(x, n).shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [0.9650, 0.0350, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [0.0000, 0.0000, 0.0000, 0.5000, 0.5000, 0.0000, 0.0000, 0.0000],\n", - " [0.0000, 0.0000, 0.5500, 0.4500, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3500, 0.6500],\n", - " [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]],\n", - " dtype=torch.float64)\n", - "torch.Size([6, 8])\n" - ] - } - ], "source": [ "import numpy as np\n", "import torch\n", @@ -166,30 +130,13 @@ "result, _ = linear_FEM_basis(x_tensor, n)\n", "print(result)\n", "print(result.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [0.9750, 0.0250, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " [0.0000, 0.0000, 0.5000, 0.5000, 0.0000, 0.0000],\n", - " [0.0000, 0.2500, 0.7500, 0.0000, 0.0000, 0.0000],\n", - " [0.0000, 0.0000, 0.0000, 0.0000, 0.2500, 0.7500],\n", - " [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]], dtype=torch.float64)\n", - "tensor([0., 0., 2., 1., 4., 5.], dtype=torch.float64)\n", - "(tensor([-1.0000, -1.0000, -0.2000, -0.6000, 0.6000, 1.0000],\n", - " dtype=torch.float64), tensor([-0.6000, -0.6000, 0.2000, -0.2000, 1.0000, 1.4000],\n", - " dtype=torch.float64))\n" - ] - } - ], "source": [ "from qulearn.hat_basis import HatBasis\n", "import torch\n", @@ -204,65 +151,23 @@ "print(vals)\n", "print(hb.position(x))\n", "print(hb.grid_points(x))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9999808199999999\n", - "0.9999808199999999\n" - ] - } - ], "source": [ "print(2*0.7071**2)\n", "print(2*0.7071**2)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1., dtype=torch.float64)\n", - "tensor(1., dtype=torch.float64)\n", - "tensor(1., dtype=torch.float64)\n", - "torch.Size([1000, 1])\n" - ] - }, - { - "ename": "IndexError", - "evalue": "index 9 is out of bounds for axis 0 with size 9", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 29\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(norms\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m#basis_functions = sawtooth_vector(x_plot, n)\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m \u001b[43mplot_basis_combinations\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_plot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbasis_functions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[3], line 12\u001b[0m, in \u001b[0;36mplot_basis_combinations\u001b[0;34m(x_values, basis_functions, n)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Generate all unique combinations of basis functions\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(basis_functions\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]):\n\u001b[0;32m---> 12\u001b[0m \u001b[43maxs\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mplot(x_values, basis_functions[:, idx])\n\u001b[1;32m 13\u001b[0m axs[idx]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbf \u001b[39m\u001b[38;5;132;01m{\u001b[39;00midx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 14\u001b[0m axs[idx]\u001b[38;5;241m.\u001b[39mgrid(\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[0;31mIndexError\u001b[0m: index 9 is out of bounds for axis 0 with size 9" - ] - }, - { - "data": { - "image/png": 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je3wx0QAAVB8LMQCAejCYzWu47LCVg0PDypd9DQBhIvEVAncF47WTWkZ8DQBANR268EJzewBALRy69d5xpN4MMQlAdZD4CoFb8TXzNW2SWGEHANSGu/AybXJxIYZ4BACoATf+HNGWUkuyMAU9NBkGAGEh8RUC98E+c2oh8UVPFQBALRCPAAD1wK1AntKaVEdrqvAaVcgAqoTEVwjcB7s70WCrIwCgFtxE13GvaZdEPAIA1Eavl/hKqaM1WXyNmASgOpK1voGoGczmNDSclyQd5211ZDUDAFB9bqLruLKt947jKBaL1fK2AABNxk1ydbQmNVjc6sgcCUC1UPFlmbutJBaTZhzhbi1hNQMAUH09h/SczOYcDWbztbwlAEATouILQC2R+LLMe6inkzqizd2/zkMdAFB97qRiekerEvFClRcN7gEA1ebOhzpG9PgiHgGoDhJflrkP8CmtKXW0FVYzKOMFANSCG5OOaEtpCivsAIAa6S1rbu/GI+ZIAKqFxJdl7kO9oy3lrWYwyQAA1MJYp2i9yilaAIAqc+dDheIA5kgAqovEl2U93kO9NMkYzOaVGc7V8rYAAE2o1Ey4vAqZiQYAoLpGVHyli/GIhRgAVULiyzKv4qs1pcmtyVGvAwBQDZnhnNfIvqMtpSlpeqoAAGqjp3xXjFvxlSEeAagOEl+WlTduTMRjmpx2e6qQ+AIAVE953JmcTnoVX8QjAEC1le+K8Xp8UfEFoEpIfFlW3uNLkndcLyvsAIBqKj9lOBGPlU7RYqsjAKDKSlsd6YMMoPpIfFnWM1iq+JIKD3eJFXYAQHWVThkuxCN3QYYVdgBAtfWOVfHF/AhAlSQnfgv8KF/NkORtLXmVii8AQBUdzIyMR6WJBvEIAFBdpT7ISWVzTvE14hGA6iDxZZnX46ttZMXXQZo3AgCqqPwErcI/i/GIFXYAQBU5juMtxnS0ppTNFxJfVCADqBa2OlpWatxYmGC4ze0PZnI1uycAQPNxJxnuCcNTvHjERAMAUD39QznlismuKa0pb0FmKJfXYJY5EoDwkfiy7NAVdnfCwQo7AKCaDhYXYtwFGOIRAKAW3MKAZDym1lRck1uSisVG/hkAhInEl2V9Q8UV9vShK+w81AEA1TNqIaYYj3qp+AIAVFF5PIrFYorHY15MYrsjgGog8WWZu5LuPswnsbUEAFAD3lbHQyu+WIgBAFRR7yGtYKRScUAfcyQAVUDiy6Lyxo2TD11hZ2sJAKCKer3EV/FUxzRbHQEA1dfjnujYVjpXrbQYQ0wCED4SXxZlhvPe8byjV9h5qAMAqserQD6052RmWI7j1Oy+AADNxdvqmC5VfFEcAKCaSHxZVJ7cmtRySI8vHuoAgCpyY5Ibh9xJRjbnKDOcr9l9AQCaS8+Au9WxvOKrkASjOABANZD4sshNbk1qSSgeLxxVQsUXAKAWDq34chdkJGISAKB6Ss3t6fEFoDZIfFl0aH8viTJeAEBt9B7S3L78FC2qkAEA1VJqbl+aI01KJySxEAOgOkh8WXToCVpS6QHPQx0AUE3u6Y1jLcYQkwAA1eIWAHSMiEepEX8GAGEi8WVRaVtJeePG0v51mgkDAKrl4FgTjVaqkAEA1TXmrhivOCBbk3sC0FxIfFl0aCNhqfRQz+VpJgwAqA7HccqqkEefokXFFwCgWsaKRxwABqCaSHxZ5PZTcfesS1J7KqFYrPjnPNgBAFWQGc4rmytUGZevsE9hhR0AUGV9Y8yRShVfuZrcE4DmQuLLor4xVjPi8Zgmt7DCDgCoHnehJRYrLMC4aG4PAKg2b1fMiOb2LMQAqB4SXxYdHBz9UJfKVjSYaAAAqsDbVtKSVDwe8173ThpmIQYAUCVuTJrUUlaBzNZ7AFVE4suisU51lEorGr2saAAAqqB02AoLMQCA2nJjzqQx+iATjwBUA4kvi3rHm2iwtQQAUEXuQsuhCzGssAMAqq1vjK2OHLYCoJpIfFnk7lGfdOhEo5UHOwCgeqj4AgDUg3zeUd9QoYH9iIovEl8AqojEl0V9xVNJpqTHqfjiwQ4AqILxtt67h6/Q4wsAUA19Q6V4M3mMxNdgNq9sLl/1+wLQXEh8WdQ77kSj2OOLFXYAQBWMdYKWRMUXAKC63MKAZDymdLI09Syv/upjMQZAyEh8WXRwsNhTZbyJBg91AEAVuAstU4oVXi56fAEAqsk70TGdVCxWOmW4JRn3EmEUBwAIG4kvi8bbWjKF5vYAgCry4hELMQCAGhpvfiSVqpLLt0MCQBhIfFnkNRM+dKsjEw0AQBWNG4/Yeg8AqKK+wyS+JlEcAKBKSHxZUn5iyagV9uJWExJfAIBqGLfHl7fVMVv1ewIANJ/ecU4ZlsoWY5gjAQiZUeJr/fr1mjVrllpbWzV//nxt3779sO+/+eab9eY3v1ltbW3q7OzUNddco8HBQaMbrlfjnVgi0UwYAFBdvW7PyUO33rdyihYAoHr6ynp8HWoyFV8AqsR34uv2229XV1eX1qxZo4ceekhz5szRkiVLtH///jHf//3vf18rV67UmjVr9Nhjj+lb3/qWbr/9dn36058OfPP1xF1dTyVGnlgi0UwYAPxigSWY8VbYyyceTDQANDPiTHW4xQGT04lRfzaFdjAAqsR34uumm27SZZddpuXLl+vkk0/Whg0b1N7ero0bN475/vvvv1/veMc7dOGFF2rWrFlavHixLrjgggmDS6NxJxCHnlgi0eMLAPxggSW48ZoJpxJxtabiI94DAM2GOFM9veP0nCx/rY94BCBkvhJfQ0ND2rFjhxYuXFj6gHhcCxcu1LZt28a85u1vf7t27NjhJbqeeuopbd68WUuXLg1w2/Wn93CNG1toJgwAlWKBJbjxenxJpb6TxCQAzYo4Uz2H2+o4iQNXAFTJ6CfQYRw4cEC5XE7Tp08f8fr06dP1+OOPj3nNhRdeqAMHDuid73ynHMfR8PCwLr/88sOukGQyGWUyGe/rnp4eSVI2m1U2668hr/t+v9f59Wpf4X4ntyRGfa/WpCOp0Ew47PuwpVrjFjWMm3+MmZmg41av4+0usKxatcp7rZIFlu9+97vavn27zjjjDG+B5eKLL67Wbded0qmOqVF/NqU1qQMHM1R8AWhKxJnqGq8CWWJXDIDq8ZX4MrF161bdcMMNuuWWWzR//nzt2rVLV111lT772c/q+uuvH/OadevWae3ataNev/vuu9Xe3m50H93d3UbXVWrnn2KSEhrq79XmzZtH/FlfVpKSGszm9f9+vlmJBjpLM+xxiyrGzT/GzIzpuPX391u+EzsacYHFva78n7XmViG3JpxR9zSppdBn5ZW+wZreb72NWaNg3Mwwbv5FdYGFOFNdvQNDkqS2VGzUvbcX+yL3DAyF9vdq1HGrJcbMDONmJsi4+bnGV+Jr2rRpSiQS2rdv34jX9+3bpxkzZox5zfXXX6+LL75YH/nIRyRJb3vb29TX16ePfvSj+ru/+zvF46OzQKtWrVJXV5f3dU9Pjzo7O7V48WJ1dHT4uWVls1l1d3dr0aJFSqVGr3zb0v/Qc9KTj6pzxlFauvS0EX82NJzXpx/8hSTpXX+2SFPbw7sPW6o1blHDuPnHmJkJOm7uf4BHQb0ssEj1kcAdzktDw4Xwvu2X96rtkEifORiXFNd9DzyogT841b/BQ9TDmDUixs0M4+Zf1BZYTBBnzP1hTyHmPP3k49rc89iIP9vzQqFw4Pe7n9Xmzc+Eeh+NNm71gDEzw7iZMRk3P3HGV+KrpaVF8+bN05YtW7Rs2TJJUj6f15YtW7RixYpxb+bQ5FYiUVhtdpyx/4M7nU4rnU6Pej2VShlPjINcW4mBbOHvMqVt9PdJpaR0Mq7McF6DOTXU5D7scYsqxs0/xsyM6bjV61g34gKLVF8J3Jf6hqQHtkqSlv3FOUrERx648v9efli/73lRbzjprVr63zprcIcF9TRmjYRxM8O4+RfVBRbiTHXdvu9B6aWX9N/nzdXSOceM+LPBh5/T/939qDqOPEpLl84L5fs36rjVEmNmhnEzE2Tc/MQZ31sdu7q6dOmll+r000/XGWecoZtvvll9fX1avny5JOmSSy7RzJkztW7dOknSeeedp5tuukmnnnqqt0Jy/fXX67zzzvMSYFFwuEbC7uuZg0PsYQeAw2jkBRYb19swlC+UfbelEmpNt4z68462wmsDWafm9yrVx5g1IsbNDOPmX9QWWIgz1dWXzUuSjmhPj7rvI9oL49M3lA/979Ro41YPGDMzjJsZk3Hz837fia/zzz9fL774olavXq29e/dq7ty5uvPOO7198nv27BkRGK677jrFYjFdd911eu6553TUUUfpvPPO0+c//3m/37qu9R2mcaNUOLXkAIkvAJgQCyzB9A25J2iN/Xd3T9HqG8pV7Z4AoJ4QZ6rncKc6ugewHORURwAhM2puv2LFinFXRLZu3TryGySTWrNmjdasWWPyrRpGb2b8E7QkaVJLYaj7mWgAwGGxwBJMX6YQZ8aaZEhSezEh1sdCDIAmRZypHjepNdauGE51BFAtoZ/q2Czch/r4K+yF1/t5sAPAhFhgMecmtNpbxqlA9hZiiEcAmhdxpjoOX/FF4gtAdYzuxAgj/UPjP9Sl0gSErSUAgDB58ahlgq2OGeIRACA8juPo4ND47WCmlFV8jdcrDQBsIPFliTuBaB93osHWEgBA+Cba6ugmxKj4AgCEqX8oJzefNVbiy41TubyjwWITfAAIA4kvS/oPs5ohlVd8MdEAAIRnoub27WwtAQBUgbvgH49JranR0862VClOMUcCECYSX5a4WxjH76ni9vhiawkAIDylCuSx49Fkt+ckW+8BACHqLTv1PhaLjfrzRDzmJb+YIwEIE4kvS/ozla2ws5oBAAhTxRXIVHwBAELUlzl8PJLKDgDLEpMAhIfElyUTVXy5D3xWMwAAYTroneo4Ts9J71RH4hEAIDzuqfeTW8dPfJUWY4hJAMJD4suS/ol6qhQnIFR8AQDC1D9Bc/v2YpyixxcAIEwHM4c/9V4qzZE4cAVAmEh8WTA0nFc2VziyZPweX6ywAwDC5y6wjFfx5VUgD+U4Ph4AEJq+CbbeS6WkGBVfAMJE4suC8hWK8SYa7go7PVUAAGHqm2CF3Y1TubyjzDDHxwMAwuFtdaTiC0CNkfiywO3v1ZKMK5UYe0ip+AIAVIMbkyaNU4FcXplMTAIAhGWiHshSKVb1EY8AhIjElwXeiY7jVHtJZT2+qPgCAITIXTVvH6fnZCIeU2uqEP6JSQCAsEx06r1UilX9xCMAISLxZUFFqxnu/nXKeAEAIXL7pBxua8lkYhIAIGRUfAGoFyS+LKhoNcPdv07jRgBAiNwqrvF6Thb+jGbCAIBw9XuJLyq+ANQWiS8LKlnNKF9d5xQtAEBY+ifo8SXRTBgAEL7+CU4Zlqj4AlAdJL4scB/qh9+/Xnio5x1xihYAIBSO43jbF8c71VEqW4yh4gsAEJL+CooDWIgBUA0kvixwJw6He6i3pUpJMZoJAwDCMJDNyS0qrmQxhngEAAhLJcUBk1iIAVAFJL4s8B7qhynjTcRjXvKL4+MBAGFwJw6x2MgFl0NNYoUdABAyd85zuHhExReAaiDxZYFX8XWYbSVSabWDU7QAAGFwK7gmtSQVi8XGfV87PVUAACFzD/U63NZ7enwBqAYSXxb0Zyeu+JI4RQsAEK6+ChoJS9JkTtECAITMnSO1caojgBoj8WVBfwU9vgp/Xqz44sEOAAiBd6LjBBXIboXyQRZiAAAh8Sq+DjNHcv+MVjAAwkTiy4K+Cho3Fv7cfbCT+AIA2HcwU1nFFz2+AABhK53qeLjm9rSCARA+El8W+K/4YkUDAGBfJf1UJHp8AQDClc87GshOnPhy41E/8yMAISLxZUGlFV+TqfgCAISor4JThqWyeMTWewBACNykl3T44gA3KTaUy2toOB/6fQFoTiS+LCiV8bLCDgCoHbeH5ESnDLvNhA+S+AIAhMBdiInFpNbU+FPO8vnTAHMkACEh8WVB+fHxhzOJU0sAACFyF2ImTxSPaCYMAAiRm8RqTyUUi8XGfV9LMq5UovDn9PkCEBYSXxZ4FV8TbHWk4gsAEKZSxVdlh60wyQAAhMHtaTxRBbJU1ueLmAQgJCS+LOgfqrDii1O0AAAhqrQC2e2pQjNhAEAY3PnORKcMS+VzJGISgHCQ+LLAW9GY4MHurnhwqiMAIAx9FVYgU/EFAAhTpT2QJeZIAMJH4iugXNlRvRMdH++uZvTR4wsAEAK3p0qlFch9mWE5jhP6fQEAmotZxRdzJADhIPEV0Mijeius+OKhDgAIgTtpaEtVFo/yjpTh+HgAgGWliq+JE1/0QQYQNhJfAbknNCbiMaWThx9O9q8DAMLkxpe2CSYa5Ykxjo8HANjW5yPxxcn3AMJG4iug8of64Y7qlUoTDSYZAIAwDGYrm2gk4jG1FBdr+rPEJACAXQMVHv4lUfEFIHwkvgLyjo6vYDXDXYEfYJIBAAiBV/E1wVZHqRS3WIwBANjmNqqfqAJZouILQPhIfAXkNbavYDWjjUkGACBElW51lKhCBgCEp9LDvyQqvgCEj8RXQO6EobWS1fVU4aFOxRcAIAylrY6VL8ZwihYAwDZ3V0wlFcjlJw0DQBhIfAXkZ3W9taUw3APZHMfHAwCs87PV0av4YjEGAGCZWxzgbmM8nLYWigMAhIvEV0Du6rqfSYbD8fEAAMscx/EmDZUsxtDjCwAQlr5iNXFbJRXIqVJxAACEgcRXQH4mGRwfDwAIy2C2tKBSUUxihR0AEBK3AnmSjwPABpkfAQgJia+ABnxsK0km4mpJcHw8AMC+8l5dlVUhF+MREw0AgGVubGmvpB0MW+8BhIzEV0ADPrY6SpzsCAAIhxuP0sm4EvHYhO93G+APMtEAAFhWSnxVfqojCzEAwkLiK6ABH83tJY6PBwCEw288clfYmWgAAGxzq5Arqfhy50csxAAIC4mvgPz0+JLKmgnzYAcAWOTGlfYKK5DdeETiCwBgm5+Kr7YWmtsDCBeJr4D8HB0vla+wD0/wTgAAKtdvWIHMCjsAwLb+TOUVX63siAEQMhJfAQ367PHlPvyZaAAAbPK99b6FhRgAgH2O43gHeVWS+HKrwkh8AQgLia+ATCcalPICAGwqbXWceFuJVNZzMpsP7Z4AAM0nM5yX4xT+vT1dwVZHTnUEEDISXwH1+z3VkWbCAIAQuHGl1W/PSSq+AAAWle9saU1OPN1050fDeUfZHIsxAOwj8RXQoGnFF4kvAIBFbgKr0ub2VCADAMLgxpVUIqZkYuLpZmtL6T3EJABhIPEV0IBhxReJLwCATQM++qlIVCADAMLhznNaK5wftSTiSsRjI64FAJtIfAXkJb7o8QUAqCH/Wx1pJgwAsG+w2Duy0sRXLBajOABAqEh8BeQ1t6fHFwCghkrN7StdiImPuA4AABv87oiRSkkyYhKAMJD4CshvxZe7BWWQhzoAwCJ3IabyrY6Fii8WYgAANg0aJL5YjAEQJqPE1/r16zVr1iy1trZq/vz52r59+2Hf/8orr+jKK6/UMccco3Q6rTe96U3avHmz0Q3XG78VX61UfAHAhIgz/vnd6ugu2AwSjwA0IeJMeNzEV2uq8qlme4rt9wDCk/R7we23366uri5t2LBB8+fP180336wlS5boiSee0NFHHz3q/UNDQ1q0aJGOPvpo/fjHP9bMmTP1zDPPaOrUqTbuv6byeceg4qv4UGc1AwDGRJwx43ero1sZ1p/NyXEcxWKx0O4NAOoJcSZcA1l/ze2l0qINiS8AYfCd+Lrpppt02WWXafny5ZKkDRs26I477tDGjRu1cuXKUe/fuHGjXnrpJd1///1KpVKSpFmzZgW76zqRGc57/15xj69iGS9bHQFgbMQZM14FcoULMe6EJJd3lM05akmS+ALQHIgz4fIbjySpLcVWRwDh8ZX4Ghoa0o4dO7Rq1SrvtXg8roULF2rbtm1jXvOv//qvWrBgga688kr97Gc/01FHHaULL7xQ1157rRKJsR+GmUxGmUzG+7qnp0eSlM1mlc1m/dyy936/11Wip3/I+/eE8hV9j2LeS32Z4VDuyZYwxy3KGDf/GDMzQcetXse7WnEmivqHhiVJbS2VhfbyXmADQzm1JGn7CSD6iDPhGywWB7Qm/SS+aG4PIDy+El8HDhxQLpfT9OnTR7w+ffp0Pf7442Ne89RTT+mee+7RRRddpM2bN2vXrl362Mc+pmw2qzVr1ox5zbp167R27dpRr999991qb2/3c8ue7u5uo+sO56WMJCWVijm6685/q+iax16JSUpo34GXG6IvQBjj1gwYN/8YMzOm49bf32/5TuyoVpyxucDiXlf+z1pwE18tcafi+0jGYxrOO3q1f1DtqTDvbrR6GLNGxLiZYdz8i+oCC3EmfH2DheKAdDJW8f22FhdfDg4MWf07NtK41QvGzAzjZibIuPm5xvdWR7/y+byOPvpo/dM//ZMSiYTmzZun5557TjfeeOO4gWLVqlXq6uryvu7p6VFnZ6cWL16sjo4OX98/m82qu7tbixYt8kqTbdm1/6D00P2a1NqipUvPquiao3a/rA2P/adSbZO0dOk7rd6PTWGOW5Qxbv4xZmaCjpv7H+BRYBJnwlhgkWqbwN3/p4SkmH778A5lnnIquiYVS2hYMd31i3t0dFu49zcekt5mGDczjJt/UVtgMUGc8ee3fyws9L/4wnPavPnZiq750/64pLh2PvKoXvvSf1m/p0YYt3rDmJlh3MyYjJufOOMr8TVt2jQlEgnt27dvxOv79u3TjBkzxrzmmGOOUSqVGlEGfNJJJ2nv3r0aGhpSS0vLqGvS6bTS6fSo11OplPHEOMi14xl2CisTbS2Jij+7o73w9xrM5htikh/GuDUDxs0/xsyM6bjV61hXK87YXGCR6iOBe+Pjv5IGBvSedy7QqZ1TK7rm8//17xrozei/LXin3nKs/793EPUwZo2IcTPDuPkX1QUW4kz4Huv+vfTs03rjCbO0dOmJFV3zwP/7nba/+Ee9/oQ3aumfvcHavTTSuNULxswM42YmyLj5iTO+El8tLS2aN2+etmzZomXLlkkqrIBs2bJFK1asGPOad7zjHfr+97+vfD6veLyQKHryySd1zDHHjBkkGol3oqOfE0uK73W3pAAASqoVZ8JYYLFxfRCD2UJPlcmt6Yrvwe3zNezEanbfJL3NMG5mGDf/orbAQpwJXyZXqDqelK78XielC+8byoXzs9MI41ZvGDMzjJsZk3Hz837fnWy7urp066236tvf/rYee+wxXXHFFerr6/NORbnkkktGNIu84oor9NJLL+mqq67Sk08+qTvuuEM33HCDrrzySr/fuu6UGglXnvhyJxnuBAUAMBJxxkzGOz6+8tBeWoyhmTCA5kGcCdegQXGAezAL8QhAGHz3+Dr//PP14osvavXq1dq7d6/mzp2rO++802sQuWfPHm8lRJI6Ozt111136ZprrtEpp5yimTNn6qqrrtK1115r729RI0YP9eJ7h3J5DefySiY4RQsAyhFnzAwOu4kv/1XIg5yiBaCJEGfC5S7w+4lHnOoIIExGze1XrFgxbinw1q1bR722YMEC/frXvzb5VnXN2+roo+KrPABkhkl8AcBYiDP+5PKOssWtJf4SX4UYlBmmChlAcyHOhGegWLXV6mOO1FaMRyS+AISBrEsAbimun4qvdLI05KywAwBsKI8nJlsdiUcAAFtM+iC7hQSDbHUEEAISXwF4qxk+HurxeEwtxSqvQVbYAQAWlFdstSZ9VHwV30s8AgDYMmDQc5IeXwDCROIrAHei4eehLklpd2sJK+wAAAvciq2WRFzxeKzi64hHAADbMgH6ILPVEUAYSHwFUDpBq/KHevn7OdkRAGCDm/hK+1yI8Sq+mGgAACwx2uroJr6o+AIQAhJfAbhbQ8r7dlXCfb97AhcAAEGYnKBVeD/N7QEAdg14izEmh60wPwJgH4mvAIJWfGWo+AIAWOAupPhdiKG5PQDANncxxk/FFztiAISJxFcAGcOKL3dFg4ovAIANg4YLMV4FMhMNAIAl7smM7kmNlWBHDIAwkfgKwHyi4VZ88WAHAASXyZoetkLFFwDALpMeX+yIARAmEl8BBK34oqcKAMAGbyEmaXjYCvEIAGBBNpfXcN6R5G8xJl22I8ZxnFDuDUDzIvEVwKBB40apVPHFCjsAwAZ3IcW4uT3xCABgQfn8xk9McudHjiMN5ViMAWAXia8AAvf4opQXAGBBaeu9z3iUpOILAGCPu80xFvM3RyqPX+yKAWAbia8ATHt8uRMNjusFANhgXIHsLcQQjwAAwXk9J5MJxWKxiq9rScTlvp2YBMA2El8BmFZ8pan4AgBY5FZs+e7xxWErAACLvMb2Pk50lKRYLObNqWhwD8A2El8BBD3VkdUMAIANxlsd3VO02FYCALBgYMj/iY6uUkxijgTALhJfAZj3+GKiAQCwx60gTvs+1ZGtjgAAe0pb7/1PM72+k1R8AbCMxFcA7kPZf8UXEw0AgD1BK76YZAAAbDDdei/RdxJAeEh8BeCW4VLxBQCoJTceGS/EsK0EAGBBxkLFF3MkALaR+AogQ8UXAKAOlCqQTSu+iEcAgOBMW8FIbL8HEB4SX4byeUdDuWA9vthaAgCwwbjiqzjJyAzn5TiO9fsCADSXUuLLYKsjPb4AhITEl6HyEty0z4lGqzfRYDUDABCcV/Hlu7l94f2OI28xBwAAU6atYKTyxRjmSADsIvFlqPyB3Orzwe6uZmRYzQAAWGB6ilZ5oowVdgBAUO78xm9hgMSuGADhIfFlyK34SsRjSib8bnWkmTAAwJ7SqY7+JhqpREyxWOHfM/RUAQAEFKTHF32QAYSFxJchb5Jh1LiRii8AgD3u6rjfiUYsFvOqvlhhBwAEFWSrozdH4lRHAJaR+DLkrWYYlPFyfDwAwKZBw+b2hWvoqQIAsCNIc3tOdQQQFhJfhmxUfPFQBwDY4FYQmyW+qPgCANhR6vFlstWxGI9YiAFgGYkvQzYqvijjBQDYUOrxFWAxhokGACCgYFsdi3MkFmIAWEbiy5B3ghYVXwCAGitVIQfYfk9MAgAEFGirY9Lt8UU8AmAXiS9DQY7qTXv71/NyHMfqfQEAmo870TDZ6phmqyMAwJJApzqWzZEAwCYSX4a8RsJGR/WWJiZDOR7sAABzw7m8hvOFRRSjrY5JmtsDAOzIuLtiAmy9Jx4BsI3El6EgFV/lExNWNAAAQQyW9YukuT0AoJZsbHUkHgGwjcSXoSAVXy2JuGKxwr9n6KkCAAigvDdXkGbC9PgCAAQVpLl9mngEICQkvgwFqfiKxWKc7AgAsMKdILQk44q5qyo+eMfHM9EAAAQUqMeX19ye+REAu0h8GQpS8SVxsiMAwI6hAJMMqez4eCYaAICAAh0AVoxjQ8QjAJaR+DJUeqibDSEVXwAAG9xDUswTX8UVdhZiAAABBdnq2ELiC0BISHwZKlV8+V/NkKj4AgDY4U4QWhJmId29joUYAEBQQbY6eokvTr0HYBmJL0NBK744tQQAYIOX+DKs+GKiAQCwJcipju5CDBVfAGwj8WXIfaibVnylkoUGxEM5Kr4AAOasJb6YaAAAAnK3zZsUB7TQCgZASEh8GQryUJfKVzQca/cEAGg+mRyJLwBAfbCy1XGYwgAAdpH4MhSkjFdiawkAwA5bPb6IRwCAIBzHsbPVkXgEwDISX4aCnFgiSS3FYMAKOwAgiKBbHTk+HgBgQ3nCymRXDPEIQFhIfBnKBO2pkij0+MqyogEACKCU+DLsOUkzYQCABeW9uYJsdcw70jBzJAAWkfgy5CasUqZbS1jRAABY4K6wG291ZOs9AMCCTNlp9SYxqbyggJgEwCYSX4YCn6LFCjsAwIKhAI2EJU7RAgDYUd4KJhaL+b6+PFnGHAmATSS+DGVzhdMYTVfYUzRvBABYYGshhq33AIAggpzoKEnJRFzxYr6MxBcAm0h8GQo80WCrIwDAAmtbHYlHAIAA3K2O6ZRZz0mJKmQA4SDxZchajy9W2AEAAQQ+bIXEFwDAgmzAhZjya5kjAbCJxJeh4Kc6MtEAAAQXtAI5zUIMAMACrwLZMB4Vri1UizFHAmATiS9DpYov/40bpVJAoKcKACCI4D2+mGQAAILLDgebH0llizHEJAAWkfgy5K5oGJ+iRcUXAMCCoVzhFC3jw1aShQkK8QgAEMRQwFYwEu1gAISDxJeh0ooGPVUAALVj61RH4hEAIAjv1PsgWx2JSQBCQOLLUNA97CkaNwIALBgKeHy8d4IW8QgAEEDQw78kigMAhMPoqbR+/XrNmjVLra2tmj9/vrZv317RdT/4wQ8Ui8W0bNkyk29bNxzH8VY0qPgCAPuaPc74EXQhprznpOM41u4LAOodscYuK6c6uosxzJEAWOT7qXT77berq6tLa9as0UMPPaQ5c+ZoyZIl2r9//2Gv2717tz7xiU/oXe96l/HN1ovyKq3Ax8ezwg4AIxBn/PG2OhpONNLF5vaOIw3nSXwBaA7EGvsyFprbt7ArBkAIfP9X8k033aTLLrtMy5cv18knn6wNGzaovb1dGzduHPeaXC6niy66SGvXrtXxxx8f6IbrgVvtJZlPNNi/DgBjI874kwna46vsOmISgGZBrLGPrY4A6lXSz5uHhoa0Y8cOrVq1ynstHo9r4cKF2rZt27jXfeYzn9HRRx+tD3/4w/rVr3414ffJZDLKZDLe1z09PZKkbDarbDbr55a99/u97nD6B4dKX+Rzymb9P5jjKlwzNJyzem+2hDFuzYBx848xMxN03Op1vBsxzrjXlf+zmjLZwqmOCTlG3z9Wtr2xbzCjlnh1qr743TfDuJlh3PyLapyRqhNrohRnKjU4NCxJSsbN7zNVzJkNZMzG6VCNMG71hjEzw7iZCTJufq7xlfg6cOCAcrmcpk+fPuL16dOn6/HHHx/zmvvuu0/f+ta3tHPnzoq/z7p167R27dpRr999991qb2/3c8ue7u5uo+vG8uqQJCUVl6O77vw3o8/4r5dikhLaf+Blbd682dq92WZz3JoJ4+YfY2bGdNz6+/st34kdjRxnpNr8HO8/kJAU0yO/eVh61ixpFVdCecV0592/0BEtdu9vIvzum2HczDBu/kUtzkjViTVRijOV+u3zhfnNi3tf0ObNzxl9xp9ejEuKa+dvH1HHi7+1dm/1PG71ijEzw7iZMRk3P3HGV+LLr97eXl188cW69dZbNW3atIqvW7Vqlbq6uryve3p61NnZqcWLF6ujo8PXPWSzWXV3d2vRokVKpVK+rh3PH18ekHb8SulUQkuXLjH6jCm7DujWJx5S++QpWrr07Vbuy6Ywxq0ZMG7+MWZmgo6bu/Lc6Oohzki1/Tn+x93bpIO9WjD/v+ndb6x8DMqtfPAXGsjm9c4z36PO15hPyPzgd98M42aGcfOPOFNiEmuiFGcqteffn5Ke2aVZr+/U0qVvMfqMe/sf0cN/ekEnvPlELX3n7MD31AjjVm8YMzOMm5kg4+YnzvhKfE2bNk2JREL79u0b8fq+ffs0Y8aMUe//wx/+oN27d+u8887zXsvnC1v8ksmknnjiCZ1wwgmjrkun00qn06NeT6VSxj9EQa49VD5WKFtOJeLGn9nWUlhSz+aduv7FsDluzYRx848xM2M6bvU61o0cZ2xcb8LtO9mWDhAjE3ENZPPKK1H1++d33wzjZoZx8y9qcUaqTqyJUpyp1LBTaGqfTpnHktaWwvQ0l49Z/XvW87jVK8bMDONmxmTc/LzfV+fBlpYWzZs3T1u2bPFey+fz2rJlixYsWDDq/SeeeKIeeeQR7dy50/vfe9/7Xp111lnauXOnOjs7/Xz7uuEd1ZtMGH8GpzoCwGjEGf/cOJI2bG4vleJZlpgEoAkQa8Jhtbk98QiARb63OnZ1denSSy/V6aefrjPOOEM333yz+vr6tHz5cknSJZdcopkzZ2rdunVqbW3VW9/61hHXT506VZJGvd5ISkfHmx/Vm+bEEgAYE3HGn1JMMl+MISYBaDbEGvu84oAgia8E8QiAfb4TX+eff75efPFFrV69Wnv37tXcuXN15513es0h9+zZo3jc/GHXCLzVjACr6+5KiLtFBQBQQJzxx0t8Bar4YoUdQHMh1tjnzmtsxKMMiS8AFhk1t1+xYoVWrFgx5p9t3br1sNdu2rTJ5FvWlcywhdUMVtcBYFzNHmf8sJL4YoUdQBMi1tg1xFZHAHWKZQwD7mqGlYc6kwwAQACZnMWKL2ISAMBQdthi4ot4BMAiEl8GrK6u5/JyHLY7AgD8cxynrMcXW0sAALVTqvgy74NMBTKAMJD4MmCzcWPh80h8AQD8K48fthZjAAAwkbVQgcxhKwDCQOLLgM1GwhITDQCAmfL4kQ5y4AoTDQBAQEPDFtvBMD8CYBGJLwNWynjLE19MNAAABrJl8SPQRMM7aZh4BAAwY2NXTIp4BCAEJL4M2Kj4SsRjihfzZjzYAQAmsvlC/IjFCnHFFFtLAABBuXOaVIA5UrKY+BqmFQwAi0h8GchaOKpX4tQSAEAwNk4ZlohHAIDgShVf5gsxqeIiznCeeATAHhJfBmxUfEmlMmBO0QIAmBh2F2ICVHtJUrJ4fZaJBgDAkDtHCrIYk/S2OlLxBcAeEl8GbOxfl6SWZEISK+wAADPuxCAZMB6xtQQAENSQhSpkt4cyrWAA2ETiy4D7UA9e8cWDHQBgzt0KEuSwlfLrh4lHAABDXnFAkFOGWYgBEAISXwZslPFKUiLBHnYAgLmshaPjJSkZd4+PZ6IBADBjow8yW+8BhIHElwEbqxmSlIqzogEAMOdODJJBK76SVHwBAILJDgdvB+OeCMmOGAA2kfgyYKviK+lVfJH4AgD45y6cuAsppryFGOIRAMDQkFvxlQxyqiOFAQDsI/FlwF2BSAes+HK3lrCiAQAw4VZoBa34StJzEgAQkJ1THd14ROILgD0kvgy4qxnJgMfHl5oJ82AHAPg3ZKGfSvn1xCMAgCk3WRVoqyM9kAGEgMSXgWHbx8fzYAcAGLAWj2gmDAAIyOapjm6/MACwgcSXgVzePUUr4NaSOKW8AABz7sJJKmAFcpKKLwBAAPm84/WJDLbVsZj4ouckAItIfBlwVzMSgbc6UvEFADCX9Sq+LG29Jx4BAAyUVwwHKQ5wF3I4ZRiATSS+DHgVXwFP0aJ5IwAgiKylHl+lw1aIRwAA/4aGyxNfwbc65p3SnAsAgiLxZcAtvQ1a8eVONHioAwBMuFsTAye+EqywAwDMlS+cBGluX17BzEnDAGwh8WXA1vHxKSYaAIAA3K0l1k4ZZiEGAGAgW3bqfTxATCpfyCEmAbCFxJcB9yGcDLjVMUFzewBAANYqvorxbIhTtAAABtz4EXxHTOl6igMA2ELiy4C9ii+a2wMAzJV6fNmKRyzEAAD8y1k40VEqJM5ixZA2ROILgCUkvgzkvIovOysaVHwBAEyUTnUMFs7Zeg8ACMLbERNwISYWi3kHiA0zRwJgCYkvA7YmGu71PNQBACaGLVV8ufGIhRgAgAlbhQFS+YErxCQAdpD4MuCV8lprJswKOwDAv6ylrSVuPCMeAQBMuPEjaI8vqRTTssQkAJaQ+DKQtfRgd5sJs8IOADBROkWLCmQAQO3kLB3+JZWKA7JsvwdgCYkvA96D3VJPlRyrGQAAA/a2OhYnGcQjAIABt8eXjYqvJD2+AFhG4suA+xAO3Nw+QXN7AIC5Us/JgFvvmWQAAAIIo8cXFV8AbCHxZcDdwx50ouGtZrDCDgAw4MaPoD2+WIgBAAThJqlsVHy1cOAKAMtIfBkoVXzZOj6ehzoAwL/ssKXm9hy2AgAIIGdzq6M3RyImAbCDxJcBr5kwx8cDAGrI7ckVeOs9Wx0BAAEMWzplWCo7ACxPTAJgB4kvA7b2sCc5Ph4AEIBXgWxpq+MQq+sAAAO5nL2Kr1TSXYwhJgGwg8SXgaylUx29xBcr7AAAA24FckvACmS3nwqTDACAiWGLze1TcZrbA7CLxJcBaxVf3lZHHuoAAP9Kx8cHrfgqXJ93pDxbSwAAPtns8eV+BlMkALaQ+PLJcRxriS+3mXCOSQYAwEDem2gE+5zynpVZtt8DAHyydep9+WfQDgaALSS+fBouS1IF3+pI40YAgLmcU4gf8VjAhZiyijG23wMA/MpZqkAu/wyKAwDYQuLLp/IJQfCtjhzVCwAwZ2trSfkKPYkvAIBf3mErFrY6lg4AIx4BsIPEl0/lJbdBS3lTCY6PBwCYKxZ8BU98lV3P1hIAgF/DofT4Yo4EwA4SXz6NrPiyc6oj/VQAACbcSUEs4FbHWCwmd67ibp8EAKBSObfHFxVfAOoQiS+fhr1JRvAVDSq+AABBuEmqRMDEl1RazGGFHQDglztHCtoDWSqr+KIdDABLSHz5NGxzNaO4VTLLQx0AYMDWqY6S5BYxsxgDAPDL1qn35Z9BxRcAW0h8+VRq3GhvNSPPthIAgAFbpzpKVHwBAMzZ7fFFPAJgF4kvn4Ytrma4W1NYzQAAmMiH0UyYxRgAgE9UfAGoZyS+fBoubksMeqKjVFbxxUMdAGDAq/jiFC0AQA25u2KsLMQkiEcA7CLx5VMYjRtZzQAAmHBbRNpobu/FJHp8AQB84lRHAPWMxJdPpR5fVHwBAGrL6lbHGH0nAQBmsl48sniqY54DwADYQeLLJ+9URwtbHd1mxPRTAQCYcJNUFgq+qEIGABjzenxZmCNR8QXANhJfPpWa2wcfuiT71wEAAbgLJza2OpZiEivsAAB/7O6KKZ7qyNZ7AJaQ+PLJfahbeKZ7ExUSXwAAE2FsdcyR9wIA+ESPLwD1jMSXT45j/+h4HuoAABNhnOo4TMUXAMCn4VB6fDFHAmCH0ZNp/fr1+v/bu//4uKo6/+Pvmclk0tCmBQvpD6oFQRCFFovtt/ijqIViWdauK2JhoVsRVqQrmF2VukqtfrUsIqJY7fqj4ipIcUX47tItxEhXkdpKKcsPaaVQqPxISuVH0qRNJjP3+8fk3pk0STPn3jM/7p3X8/HgUTK59+bOyfR+ej7nnM+ZPn26GhoaNGfOHG3ZsmXEY7///e/rXe96lw4//HAdfvjhmj9//iGPr3bu8zducQctitsDwGC1HGdMZEuwqyMdDQC1glhjDzW+AFQz48TXunXr1NLSohUrVuihhx7SjBkztGDBAu3Zs2fY4zdu3KjFixfrvvvu06ZNmzRt2jSdddZZev755wPffCV4o+sWOhkUtweAoWo9zpjI2FzqSEcDQA0h1tjVbzMeUXMSgGXGia8bbrhBl156qZYuXaqTTjpJa9asUWNjo9auXTvs8bfccos+8YlPaObMmTrxxBP1gx/8QNlsVm1tbYFvvhKy3rKS4NeiuD0ADFXrccaEzcGYOmYhA6ghxBq7vBlfFmtOMhADwJY6k4P7+vq0detWLV++3HstHo9r/vz52rRpU1HX6OnpUTqd1hFHHDHiMb29vert7fW+7uzslCSl02ml02mTW/aONz1v5Ov1S8plDINeM5vJSMoFClv3Z4vtdqsVtJs52syfoO1Wre0dxjjjnlf4Z7m4o+FOtj/wz3b7Kr19wa9VDP7u+0O7+UO7mYtqnJHKE2uiEmeK1ZfO9WvkZIPfo5OLbf39wa9V7e1WjWgzf2g3f4K0m8k5RomvvXv3KpPJqLm5edDrzc3N2r59e1HX+OxnP6spU6Zo/vz5Ix6zatUqrVy5csjr9957rxobG01u2dPa2urrvIM9+nJMUkKvvfaa1q9fH+haXWlJqlPWke6+e70sDNhbZ6vdag3tZo4288dvu/X09Fi+EzvCHGek8n+Oe3sTkmK6/7e/1U7/ty1Jeu3V3LX+sHWr0s+Ub5Sdv/v+0G7+0G7mohZnpPLEmqjEmWI990JcUlzbn/ij1r/yeKBrbX8x19967vnntX79n63cX7W2WzWjzfyh3fzx024mccYo8RXUtddeq9tuu00bN25UQ0PDiMctX75cLS0t3tednZ3eOvqmpiajn5lOp9Xa2qozzzxTyWTS9727kn/cI+14WEccPkELF84JdK1Xe9L6/IP3SZIWnH226hLVs8mm7XarFbSbOdrMn6Dt5o48R00l4oxUuc/xF7b9Wurv1xnz5unYIw8LdK1b2/+gp7te0YyZp2rhyZMs3eHI+LvvD+3mD+1mjjgzsmJiTVTiTLH+85Vt0ssvaeYpJ2vhaUcHutbe3+/WL5/ZrkmTJ2vhwhmBrlXt7VaNaDN/aDd/grSbSZwxSnxNnDhRiURCHR0dg17v6OjQpEmH/kfy9ddfr2uvvVa/+tWvdMoppxzy2FQqpVQqNeT1ZDLp+0MU5NxC8YHkVF0iHvh6hW8xXlenZF0i0PVKwVa71RrazRxt5o/fdqvWtg5znLFxvim3/EmqPvjPTSYGYlAseHwz+rn83feFdvOHdjMXtTgjlSfWRCXOFCur3NKV+mRd4Purd/tEsZi191qt7VbNaDN/aDd//LSbyfFGU4zq6+s1a9asQUUc3aKOc+fOHfG86667Tl/+8pe1YcMGnXbaaSY/supkBjYXidnYOr7gGhS4BwDijCmbuzrG2dURQI0g1tjXb7G4vdvPon8EwBbjpY4tLS1asmSJTjvtNM2ePVs33nijuru7tXTpUknSxRdfrKlTp2rVqlWSpH/913/VNddco1tvvVXTp09Xe3u7JGns2LEaO3asxbdSHt6ujhbqcRV2VHiwA0BOrccZE96ujhaCkttZYft4ALWAWGOXGztsDMS416B7BMAW48TX+eefr5deeknXXHON2tvbNXPmTG3YsMErDrl7927F4/mJZN/97nfV19enD33oQ4Ous2LFCn3xi18MdvcV4Ca+bD7UJRJfAOCq9ThjIuvO+LIwC9kNSQ7hCEANINbY5fZl4hbjUZb+EQBLfBW3X7ZsmZYtWzbs9zZu3Djo62eeecbPj6ha+RlfLHUEgFKp5ThjIj/jK/i13KUlhCMAtYJYY48bO6wsvffiEQEJgB3Vs41gSGQt1viKx2NyL0PiCwBgwnEcb3aWzRlfdDQAAKYci+Vg3MRXhnAEwBISX4a8pY4WHuq567gPdp7sAIDiFY6X2FlakruGQzwCABhyY5KVDcDixCMAdpH4MmRzqaOUf7Az4wsAYKIwbtgobh9nqSMAwCebfSRWxACwjcSXIff5a6OTIZH4AgD4U7gk0UZNlRhLHQEAPnl9JAtdpPyujsQjAHaQ+DKUtbh+XZLcy/BcBwCYKBwwsVPjixlfAAB/HIszvrx4lA18KQCQROLLWNbiVr2F16GfAQAwUVgb0saujtRUAQD45U4OsNFFYldHALaR+DJke6mjWFoCAPAha3nGFzVVAAB+ubOz7Mz4yv3J5l8AbCHxZch2cfv8LlpWLgcAqBGDljpS3B4AUEE2+0j5Gl+BLwUAkkh8GctkLdf4GrgOS0sAACYyBctKbGwfH2cGMgDAJ8dicft8jS/iEQA7SHwZch/qNpaVSNT4AgD4U7J4ROILAGAoX+PLwkAMuzoCsIzElyGbD3Upv6sjD3YAgImM5c1WYix1BAD4lF/qGPxacWpOArCMxJehjMWHupTvaJD3AgCY8BJfliI5Sx0BAH55Sx0tdJIS9I8AWEbiy5C3tMRS5ouOBgDADzdu2F7qyAA7AMCUzRlf7sQAdnUEYAuJL0NukUVrSx294vZWLgcAqBH5GV92B2Ko8QUAMOUOmtjoI3m7OjISA8ASEl+GbC91jDOVFwDggzfjy1JAytf4IiABAMzkZ3yxyzCA6kPiy1DW8lJHitsDAPzIZHN/stQRAFBpXo0vG8Xt4yx1BGAXiS9DjsXRDKmguL2VqwEAaoXtpY6JgX8RMBADADBld8aXu9Qx8KUAQBKJL2MZr8aXnevFmMoLAPChVMXtCUcAAFNuTLIRkhIsvQdgGYkvQ95SRzoaAIAKsrmDllR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HdvOHdjNHnLGLOFMZtJs52swf2s2fIO1mEmdCkfhypwM3NTX5Snw1NjaqqamJD6AB2s0f2s0cbeaPrXZjuUVOkDgj8Tn2gzbzh3bzh3YzR5yxizhTGbSbOdrMH9rNHxvtVkycYcE9AAAAAAAAIonEFwAAAAAAACKJxBcAAAAAAAAiicQXAAAAAAAAIonEFwAAAAAAACKJxBcAAAAAAAAiyTjx9Zvf/EbnnnuupkyZolgspjvvvHPUczZu3Ki3ve1tSqVSOu6443TzzTf7uFUAQC0gzgAAAACwxTjx1d3drRkzZmj16tVFHb9r1y6dc845es973qOHH35YV111lT72sY/pnnvuMb5ZAED0EWcAAKXEAAsA1JY60xPe//736/3vf3/Rx69Zs0bHHHOMvv71r0uS3vzmN+v+++/XN77xDS1YsMD0xwMAIo44AwAoJXeA5aMf/ag++MEPjnq8O8Dy8Y9/XLfccova2tr0sY99TJMnTybOAEAIGCe+TG3atEnz588f9NqCBQt01VVXjXhOb2+vent7va87OzslSel0Wul02ujnu8ebnlfraDd/aDdztJk/QdstSu1d6Tjjnlf4J0ZHm/lDu/lDu5mLcpxhgAUAakvJE1/t7e1qbm4e9Fpzc7M6Ozu1f/9+jRkzZsg5q1at0sqVK4e8fu+996qxsdHXfbS2tvo6L4wcR4rF7FyrltrNJtrNHG3mj9926+npsXwnlVMtcUbic+wHbeYP7eYP7WaOOMMAS5gVtpvjOIrZ6iRFGJ81f2g3f4K0m8k5JU98+bF8+XK1tLR4X3d2dmratGk666yz1NTUZHStdDqt1tZWnXnmmUomk7Zvter822926Qf3P6OffeztOu6osb6vU2vtZgvtZo428ydou7n/AK9VNuOMxOd4OK/2pPV3a/+gk6eO16q/ecuQ79Nm/tBu/tBu5ogzeQywhN9F32nT7n0xfeaUjOoTlb6bcOCz5g/t5o+fdjMZYCl54mvSpEnq6OgY9FpHR4eampqGDRKSlEqllEqlhryeTCZ9/2MlyLlhcn3rk5Kkz931R/3yE+8IfL1aaTfbaDdztJk/ftstSm1dLXHGxvlR8sMHntKOjn3a0bFP13945ojH0Wb+0G7+0G7miDP+MMBSHdLptO69t1Vb9+b2dGt609v13hOOrPBdVTc+a/7Qbv4EaTeTAZaSJ77mzp2r9evXD3qttbVVc+fOLfWPrmm79nZX+hYAoCyIM9Xple4+7/8zWUeJOMtLAIQTAyzh1t2f//9Uso72KxKfNX9oN3/8tJvJ8XHTG9q3b58efvhhPfzww5Jyu5w8/PDD2r17t6Tc6MbFF1/sHf/xj39cTz/9tD7zmc9o+/bt+s53vqPbb79dn/rUp0x/NACgBhBnosGR4/3/vgP9hzgSAKrb3Llz1dbWNug1BljC47X8OIwOpDOVuxEAFWOc+HrwwQd16qmn6tRTT5UktbS06NRTT9U111wjSXrxxRe9zokkHXPMMbr77rvV2tqqGTNm6Otf/7p+8IMfsAMKAGBYxJlo2J/Oev/feYBCrwCqBwMstWVff37GcScDMUBNMl7qeMYZZ8hxnBG/f/PNNw97zrZt20x/FAylM9nRDwKAKkeciYbO/flk12v705pWwXsBgEIPPvig3vOe93hfu7W4lixZoptvvnnEAZZPfepT+uY3v6mjjz6aAZYQ2V+Q6yqMTQBqR1Xu6gh/ugpGMA7RZwQAoOQKZ3l1McIOoIowwFJbDhSsbmTGF1CbjJc6onoVjmD09PUfMqADAFBKhTGJpY4AgEphxhcAEl8RUtixSGcc9faz9BEAUBmFo+rM+AIAVMr+TL7GF/EIqE0kviLktYNGMBjRAABUyqAZX8QjAECFDJrxxQxkoCaR+IqQzv2DRzBYww4AqIQD6cygWcd0NAAAlbK/sMYXAzFATSLxFSEHdyzoaAAAKuHgpSQsLQEAVEphCCIeAbWJxFeEdB2U6OLBDgCohIPjESPsAIBKKazxxcQAoDaR+IqQgxNddDQAAJVw8FJ7BmIAAJXCro4ASHxFCEtLAADV4OCOBSPsAIBKKazx1dXbr2zWqdzNAKgIEl8RQo0vAEA1IB4BAKpF4VwAx5G6+5gcANQaEl8R4s7wStXlfq1M5QUAVIK7y/DhjUlJzEAGAFSG4ziDZnxJQ5fjA4g+El8R4hYTnjphzMDXPNQBAOXnzvA6+vDG3NcMxAAAKqC7LyNHueL2Y1N1kohJQC0i8RUhbqJr6uG5xBdLSwAAlbBvIB5NmdAgKRefHIeaKgCA8nL7R8lETEeOSw16DUDtIPEVIe5DfMp4ZnwBAConPwM5N+OrP+tofzpzqFMAALDOnd01rqFOTWOSg14DUDtIfEWI19FwZ3zxUAcAVIA78HJUU0qJeG6JSed+BmMAAOXV1ZuLPeNSSTU1DCx1ZFUMUHNIfEWE4zj5pY7U+AIAVJDX0Wio8zoaXXQ0AABl5haybxpTp6YGZnwBtYrEV0QcSGfVn83VT6HGFwCgktwk17iGpMa5HQ1iEgCgzNyJAONSdWoaUzfoNQC1g8RXRLidjEQ8puamfDFhAADKbbiOBtvHAwDKLT8QUzDji4EYoOaQ+IoIt0MxNlWn8QOFG/f19qs/k63kbQEAapCX+GpgaQkAoHK6vKWOSY1za3xRcxKoOSS+IqKzYDTDfahLueQXAADltM+r8VXQ0WDGFwCgzDoHzUDODcR09TIQA9QaEl8RkR9dTyqZiGtMMjHodQAAyiG32crQpSUUtwcAlNuwSx2Z8QXUHBJfEVH4UC/88zWWlgAAyqi3P6t0JrfZytiGunxxezoaAIAyG7T03qs5Sf8IqDUkviLCW78+kPDypvIy4wsAUEZu3InFpLH1hbto0dEAAJRXp9dHyu8yTP8IqD0kviKicOv43J+MaAAAys+NR2Pr6xSPxwp20aKjAQAoLzZbASCR+IqMwoe6pIKaKnQ0AADl48adsQctvaejAQAot0E1vgqWOjqOU8nbAlBmJL4i4uDEFx0NAEAlDBmIGUNxewBAZRTGJHdlTDrj6EA6W8nbAlBmJL4iovOgpY7U+AIAVMK+3oOW3qfqBl4nHgEAyquwxtdh9QnFYrnXiUlAbSHxFREjzvhihB0AUEZuJ2PsQMLLXfK4j4EYAEAZ9fZn1Nufm9k1rqFOsVjMi00kvoDaQuIrItwlJE0HjbB381AHAJTRwQMxbieji3gEACijwpUvbizyZiEzGAPUFBJfEUFHAwBQDfZ58Sg3EOPO+Oru7aeYMACgbNx4lIo7SsRzaxzdmNTVy6oYoJaQ+IqIriEdjdyfjGYAAMopPwPZHV3PxaOsI+1PZyp2XwCA2uIuZ2xI5F87jBlfQE0i8RURB3c0xqZyT3iWOgIAyqnroBpfDcm4N9JORwMAUC5uPBroHkkSNb6AGkXiKwIcx/GKCXszvgZG2HmoAwDKyY077tL7WCymw+pzgzEsvwcAlEv3MDO+3NhEHwmoLSS+ImB/OqNMNlc3xavx5a5fZ3QdAFBG7m7C7kBM4f8z4wsAUC5uciuVyNeX9OogE4+AmkLiKwLcB3ciHlPjwKg603gBAJXgLXUsWFtCTAIAlFvXMDO+WBUD1CYSXxHg1vcam6pTLBbz/l/KPdTZRQsAUC5d3oyvgsQXs5ABAGXmzjIelPhqoLg9UItIfEVAfkfHoZ2MTNZRb3+2IvcFAKg97ih6U8FSR2Z8AQDKbV9vbiBmUI0v4hFQk0h8RYD74HY7FpLUmExoYPIXI+wAgLI51GDMvoHZYAAAlFp3b0bS8DO+6B8BtYXEVwR0D5P4isdjGlvPiAYAoHz6M1n19OU6GoUxyR1h7x74HgAApeYmt4Yrbu/OBgNQG0h8RYD7UD+soJMhsYYdAFBe7ui6NHhXR3bRAgCU23BLHb3+ERMDgJpC4isChpvxJeUTYV2MaAAAyqBzYCljqi6u+rr8PzEOY4QdAFBm+4bZ1dGr8cVADFBTSHxFQPcwy0oKvy4cgQcAoFSGq+9V+DUdDQBAuRxyV0dmfAE1hcRXBLgP7oOXOnodDUbYAQBl0DUw46twmaPEro4AgPLLz/gaWuOLpfdAbSHxFQHuaMbYVGLQ62OZygsAKKPhdhmW2EULAFB+bkxKDVrqmBuY6e3Pqq8/W4nbAlABJL4ioHuEGV/eiAYj7ACAMsjPQB5hIIZ4BAAok+GWOhbGp25iElAzSHxFgDfC3jB8cXtmfAEAysGtKTk2NXipo7v0nk4GAKAcslnHq4Nc2EWqS8Q1JplLfjEYA9QOEl8R0N03/NISOhoAgHLK7zJ88IyvXCKMTgYAoBzc/pE0eMaXxPJ7oBaR+IoAd0bXYfUsdQQAVM5Im624S0voZAAAysGNR8lETHWxwd8bx/J7oOaQ+IqAkToaY9k+HgBQRt0jFLenmDAAoJwKJwbEDkp8eX0kdr4HagaJrwhwa6qMaxh+xhejGQCAcnCXlow040ti+T0AoPS6Rlh6n3uNpY5ArSHxFQGj7epI4gsAUA77BgZiDo5HFBMGAJSTO+Pr4BnIha8Rj4Da4SvxtXr1ak2fPl0NDQ2aM2eOtmzZcsjjb7zxRp1wwgkaM2aMpk2bpk996lM6cOCArxvGYI7jaJ83ws728QCigTgTTiMVt5coJgwAKJ/uEXa9L3yNcjBA7TBOfK1bt04tLS1asWKFHnroIc2YMUMLFizQnj17hj3+1ltv1dVXX60VK1boiSee0A9/+EOtW7dOn/vc5wLfPKSevowcJ/f/B49o8FAHEEbEmfAaqeaklC8mXLjTFgBUCgMs0dZVRDxicgBQO4wTXzfccIMuvfRSLV26VCeddJLWrFmjxsZGrV27dtjjH3jgAb3jHe/QBRdcoOnTp+uss87S4sWLRw0uKI47mhGPyVtG4hrH9vEAQog4E14jLb2XGIwBUD0YYIk+b6lj/cjxiBnIQO0Y+iQ4hL6+Pm3dulXLly/3XovH45o/f742bdo07Dmnn366fvrTn2rLli2aPXu2nn76aa1fv14XXXTRiD+nt7dXvb293tednZ2SpHQ6rXTabPcN93jT88LilX25kabDUnXq7x/88K5P5KaC9fRldKC3T4l4bMj5I4l6u5UK7WaONvMnaLtVa3uHMc645xX+WavcjkZDYmhbHFafG5x5tfvAoHau9TYzRbv5Q7uZi2qckQYPsEjSmjVrdPfdd2vt2rW6+uqrhxxfOMAiSdOnT9fixYu1efPmst43irfPW+o4XHF7JgcAtcYo8bV3715lMhk1NzcPer25uVnbt28f9pwLLrhAe/fu1Tvf+U45jqP+/n59/OMfP+QIyapVq7Ry5cohr997771qbGw0uWVPa2urr/Oq3e59klSnRDat9evXD/pebsf43K/4l//132o0+m3nRLXdSo12M0eb+eO33Xp6eizfiR1hjjMSn+OXuxKSYtq2ZZP2PD74e/teiUuK6/dbH1b8uW3e67XeZn7Rbv7QbuaiFmcYYKkNr/Xk2n5MXW6BU2G7jUnmJgN07u+jPYfBZ80f2s2fIO1mco6PVIiZjRs36qtf/aq+853vaM6cOdq5c6euvPJKffnLX9YXvvCFYc9Zvny5WlpavK87Ozs1bdo0nXXWWWpqajL6+el0Wq2trTrzzDOVTCYDvZdq9PunX5YefVCvGz9WCxe+Y8j3r36wVemMo3fMe68mj28o+rpRb7dSod3M0Wb+BG039x/gUVDpOCPxOXZd/eCvJGW14H1n6PVHDE4g3tfzqB595UVNP/7NWvjO6bSZT7SbP7SbuajGGQZYasMTT+cGWzqee0aaNrjdnnopJimhZ55vHzJxAHl81vyh3fzx024mAyxGia+JEycqkUioo6Nj0OsdHR2aNGnSsOd84Qtf0EUXXaSPfexjkqSTTz5Z3d3duuyyy/Qv//IviseHlhlLpVJKpVJDXk8mk77/sRLk3Gp2ILdzvMY1DP/+xjUk9XJ3nw5k5Ov9R7XdSo12M0eb+eO33aq1rcMcZ2ycH2aZrKP96awkacJhDUPaoamxXpK0P50d9L1abrMgaDd/aDdzUYszfjDAEj6ttz8idbRrxkknSF3bB7Vbavse/WTnw2oYN0ELF/6fCt9p9eGz5g/t5k+QdjMZYDFKfNXX12vWrFlqa2vTokWLJEnZbFZtbW1atmzZsOf09PQM6XQkErm11o67HSF829ebm9538I6OrrGpOr3c3UfxRgChQJwJr8LdGoctbj/wWhc1VQBUEAMstaFnYCCmqbFe6hrcbhMOy62C6e7N0JaHwGfNH9rNHz/tZnK88a6OLS0t+v73v68f//jHeuKJJ3T55Zeru7vbKw558cUXD1ozf+655+q73/2ubrvtNu3atUutra36whe+oHPPPdfrmMC/fb25KV+HpYZvy8PYrhdAyBBnwsnd0bEuHlOqbug/L9xdtLqJRwAqqHCAxeUOsMydO3fYcxhgCR9vV8dDDMTQPwJqh3GNr/PPP18vvfSSrrnmGrW3t2vmzJnasGGDt05+9+7dgwLD5z//ecViMX3+85/X888/ryOPPFLnnnuuvvKVr9h7FzXsUFvHS9K4FNvHAwgX4kw4FcajWGzoLsJ0NABUi5aWFi1ZskSnnXaaZs+erRtvvHHIAMvUqVO1atUqSbkBlhtuuEGnnnqqt9SRAZbq5s4uHpuqU9dB33PjUc/ABAIA0eeruP2yZctGXHKycePGwT+grk4rVqzQihUr/PwojMLtaIwbaaljg9vRYHcJAOFBnAkfdwbySEvvG+vdGV90NABUFgMs0dd9iMRX48BKme6+fjmOM+xgDYBoKfmujigtt3bXSDO+3NfpaAAASik/42uEpff1udd7+pjxBaDyGGCJtn29Iy91PGxgICbrSL39WTUkmbUHRJ1xjS9Ul9GWOtLRAACUw75R4lEjAzEAgDJxy7w0DjMYM6Yg0cXye6A2kPgKOXcXrVGXlvTR0QAAlE73IUbXJQZiAADl0defVV8mt6ujO7urUDweU6MbkxiMAWoCia+Q6zrEjiVSfslJD6MZAIAS8mYgD9PJkAqW3jMQAwAoof0FccZNcB0sPzmAPhJQC0h8hdxoSx2Z8QUAKAe3uP3IS+/dXbToZAAASqcnnYszyURM9XXDd3fHppiFDNQSEl8h1z3KLlqH8VAHAJRBfqnjCKPrbjxKZ5TNOmW7LwBAbXH7R40jzEAu/N4+ljoCNYHEV8jtG2UXLbaPBwCUw2jF7d0ZX44jHegnJgEASsMd8D9shGWOEuVggFpD4ivk3I7GuAaKCQMAKme0pfcNybhiMfdYEl8AgNLwZnyNEI8kysEAtYbEV4g5jjN6jS+2jwcAlMFouwzHYrF8nS8GYwAAJWI044t4BNQEEl8h1tufVf9AnZSRl5bwUAcAlN5oxe2l/O5a+1haAgAoEXcW16FqfB1GORigppD4CrHugo7DSNvHM40XAFAO+w6kJY1c3F7KJ8V6iEkAgBLpGaUGcu57buKLgRigFpD4CjG349CQjCsRjw17DIUbAQDl4MakYmZ80dEAAJRKMTO+vHjEqhigJpD4CrGeoh7qA6PrbB8PACght/NQzNISZnwBAErFZMZXD0sdgZpA4ivE8p2M0Qs3sn08AKCU9nszvkaOSY0pZnwBAEqLGV8ADkbiK8T2ew/1kTsZDXUJto8HAJSct318khlfAIDKKW5XR+IRUEtIfIVYMUsd4/GYGpPs7AgAKJ1s1tH+dC4mjTlER4MRdgBAqXkDMYeoOekOxLDLMFAbSHyFWE8RSx2l/EOfGV8AgFIoXEp/6OX31FQBAJRWMTO+3KX3TAwAagOJrxArZsaXlH/o82AHAJRC4cDKmCQzvgAAlVNMjS9v6T0DMUBNIPEVYm5x4FFnfA082LtZww4AKAG35uSYZELxeGzE45jxBQAotWJ2dWQgBqgtJL5CrJji9lL+od/DGnYAQAn0pIsbiDmMjgYAoMSKmvFFKRigppD4CrGedHFLHZnxBQAoJW/p/SFG13PfdzsaJL4AAKXh1fg6RExyv9fd1y/HccpyXwAqh8RXiPUUudTxMIo3AgBKyF262JgcreYkAzEAgNLydnUsosaX40gH0tmy3BeAyiHxFWJFj7DXM5UXAFA67sDKmFF3GWYgBgBQWvldHUdOfBVuxMLyeyD6SHyFmJf4OsQOWhK7OgIASmt/usiak+yiBQAooWzWKWpyQDwe82IWMQmIPhJfIeYmskat8UXxRgBACfUUUUg4932K2wMASscdiJEOPeNLysesfdSdBCKPxFeIFbvUkRlfAIBS6i665iQzvgAApeMOrMRiUkPy0F3dsSy/B2oGia8Qy4+wF1nji2LCAIAS2F9kPDqsnl20AACl4w6sHFZfp1gsdshj6SMBtYPEV4gVu9TR29WRabwAgBLoGVhaMnpx+1y8yjpSbz+7aAEA7OoucrMViT4SUEtIfIWY+YwvHuoAAPvcGV+j1VMZvIsWI+wAALt6vHg0euKLGV9A7SDxFWLFJr680Qwe6gCAEnBrfI02wp6Ix7zkFzVVAAC25WtOHnogRsr3kbqZ8QVEHomvENtf9C5a7q6OPNQBAPa5Sx1HG4iRCpeWMBgDALDLm/E1yuZfEqtigFpC4iuk0pms+jK5+iijFxMe2EWLGV8AgBIodqmjxNISAEDpmMz4cvtQB4hHQOSR+AqpwiTWqDO+mMYLACihYpc6Svk6XwfSdDQAAHaZzPjKL70nHgFRR+IrpNzaKHXxmOrrDv1rdEfg99PJAACUwH6DpY4NA8fsp6MBALCsp8hSMFJ+sIY+EhB9JL5Cyn2om4yupzOO0hm2jwcA2GUSkxqTdDQAAKXhTg4oZiBmDPEIqBkkvkLKpJ5KQ33+18zSEgCAbSYxKT/CzkAMAMAukxlfjcxABmoGia+QyhduHH00oz4RVzyW+38e7AAA27oZYQcAVAE3trix5lAaiEdAzSDxFVLe1vFFFG6MxWLeqAcPdgCAbUbL7xlhBwCUyH4vHo3ezXX7RxS3B6KPxFdI9fQOJL6So0/jlRjRAACURibrqK8/t2yxqKWO7OoIACiRfOKrmKX3ua4w8QiIPhJfIeUWbixmdD13XHzgPB7sAAB73HgkFVncnl20AAAlYrLUcUySGV9ArSDxFVLuQ/2wIpY6SgUj7DzYAQAWuR2GeExK1Y3+zwpmIAMASsUo8cXSe6BmkPgKqe5e96Fe3FLHMdT4AgCUQOEOWrFYbNTj6WgAAErFpMYXm60AtYPEV0jtH1haUvyMr9yvmgc7AMCmHoMdHQuP25/OluyeAAC1KT/ja/TJAY0MxAA1g8RXSHUb7KAl5Uc0WMMOALApP+OruHjEUkcAQKnsN+gjFcajbNYp6X0BqCwSXyHldTSKXOrobtfLriUAAJt6DHbQkgqWljAQAwCw7IBBja/CAZvefmYhA1FG4iuk9hsuLWmgowEAKAHTeOQex0AMAMA2bzCmiMRXQ8ExhTsUA4geEl8h5a1fL3ap40CBR5Y6AgBsMl3qSDFhAEApOI5j1EdKxGPebsTEJCDaSHyF1IGBosANRYxmSPmOBiPsAACbTEbXJamBYsIAgBIoXK5Y/OQAYhJQC0h8hdR+g/XrUr72CqMZAACbDhjOQGZXRwBAKRQmr4rtIzUyCxmoCSS+QsrtaDQki/sVsqsjAKAUvHhUxwxkAEDl9AzElfq6uBLxWFHnuLOQ6SMB0UbiK6RMdizJHcf6dQCAfe7Se9NlJT3pjBx2jwcAWLLfcOm9VDgLmT4SEGW+El+rV6/W9OnT1dDQoDlz5mjLli2HPP7VV1/VFVdcocmTJyuVSulNb3qT1q9f7+uGkeM+nFPFTuMdWOp4gNEMACFAnAmPfDwym4HsOFI/iS8AgCWmEwMKj6XGFxBtxomvdevWqaWlRStWrNBDDz2kGTNmaMGCBdqzZ8+wx/f19enMM8/UM888o//4j//Qjh079P3vf19Tp04NfPO1zBthNywmzDReANWOOBMu5jOQ88cRkgBUCgMs0WO6633u2IE6yAQkINLqTE+44YYbdOmll2rp0qWSpDVr1ujuu+/W2rVrdfXVVw85fu3atXr55Zf1wAMPKJlMSpKmT58e7K7hzdwyrfHFNF4A1Y44Ey77vZqTxXU06hJx1Sfi6stk1Ud9ewAV4A6wrFmzRnPmzNGNN96oBQsWaMeOHTrqqKOGHO8OsBx11FH6j//4D02dOlXPPvusJkyYUP6bx4hMdxnOHZvrS/XQRwIizSjx1dfXp61bt2r58uXea/F4XPPnz9emTZuGPef//b//p7lz5+qKK67QXXfdpSOPPFIXXHCBPvvZzyqRKP6hhMEO9JuNaFBMGEAYlCvO9Pb2qre31/u6s7NTkpROp5VOp43v2z3Hz7lht7+3X5JUHy/+/Tck84mvWmyzIGr5sxYE7WYuaJtVc1szwBJNXo0vgxlflIMBaoNR4mvv3r3KZDJqbm4e9Hpzc7O2b98+7DlPP/20fv3rX+vCCy/U+vXrtXPnTn3iE59QOp3WihUrhj3HZockiv/Q6c9klc7kCqPUySnqvdXHc8f39PUXdXwU260caDdztJk/Ue2QlCvOrFq1SitXrhzy+r333qvGxkbf99/a2ur73LDa/UJcUlx/euJxrX/5saLOiWUSkmLqy9Rmm9lAu/lDu5nz22Y9PT2W78QOBliia9+BPklSqi42pL1Garf6RG73x64DfbTtAD5r/tBu/gRpN5NzjJc6mspmszrqqKP0ve99T4lEQrNmzdLzzz+vr33ta2XtkETpHzoHMpL7q9vY1qpiBjWe786d82pXj1E9gii1WznRbuZoM3+i1iHxw0+cWb58uVpaWryvOzs7NW3aNJ111llqamoyvod0Oq3W1ladeeaZ3myAWnFbx4PSKy9r9qyZWnjK5KLO+caf7tdrf+lRX1Y12WZB1PJnLQjazVzQNnMTPdWGAZbo+kNHTFJCnS/vHdLfGandOp7PDd48vv1Jrd+/o/Q3GSJ81vyh3fzx024m/RmjxNfEiROVSCTU0dEx6PWOjg5NmjRp2HMmT56sZDI5aDTkzW9+s9rb29XX16f6+voh59jskETxHzp79/VKW/5HkvSBv3q/YrHYqOc885duXffI75SNJ7Vw4YJRj49iu5UD7WaONvMnqh2ScsWZVCqlVCo15PVkMhnocxj0/DDq7c8V6jqsob7o9+4WE05nYzXZZjbQbv7Qbub8tlmU2pkBlnDoeOBZ6ekdmn70FC1ceIqk0dtt+6+e1MYXd2nKtDdo4cI3l/uWqxKfNX9oN3+CtJtJf8Yo8VVfX69Zs2apra1NixYtkpQLBG1tbVq2bNmw57zjHe/Qrbfeqmw2q3g8VzzwT3/6kyZPnjxsZ0QqTYckSv/Q6XdyU/oakvER2/Bg48Y0SMrV+DJphyi1WznRbuZoM3+i1iEpV5yBPfsNdxnOHZv7PfVSUgVAmTHAEl0D4UiHpYa20UjtNrYh97vrzTi060H4rPlDu/njp91Mji9uS8ACLS0t+v73v68f//jHeuKJJ3T55Zeru7vbKw558cUXD1ozf/nll+vll1/WlVdeqT/96U+6++679dWvflVXXHGF6Y/GANOt46V8kcf+rKO+frbRAlC9iDPh0mu4q6OULyacJhwBKLPCARaXO8Ayd+7cYc95xzveoZ07dyqbzT+0GGCpPn6K27v9qR6K2wORZlzj6/zzz9dLL72ka665Ru3t7Zo5c6Y2bNjgrZPfvXu3N+IuSdOmTdM999yjT33qUzrllFM0depUXXnllfrsZz9r713UmAMDPQWTTkZhkmx/OqP6OuOcJwCUBXEmXPb7GIxx41cfiS8AFdDS0qIlS5botNNO0+zZs3XjjTcOGWCZOnWqVq1aJSk3wPLtb39bV155pf7xH/9RTz75pL761a/qk5/8ZCXfBg7S42tXx9yx7sQCANHkq7j9smXLRlxysnHjxiGvzZ07V7///e/9/CgMw08nI5mIKRGPKZN1dCCd0fgxTL8EUL2IM+FxwJvxVfyAitvRIPEFoBIYYImmIAMxB5iCDERayXd1hH3uQz1l8FCPxWJqTCbU1dvPVF4AgDX7fSx1dJNk9DMAVAoDLNHjpxxMamAVDDO+gGhjvVsI5R/qZr++hnp3DXu/9XsCANQex3F8Lb9P1eWOTWdH35UYAIBiuDW+GgyWOnozvvpJfAFRRuIrhA74GF3PHT+wixbF7QEAFhTGE5Oljsz4AgDY1jPQR2o0GYhx+0cEJCDSSHyFkJ9pvJLUUEfxRgCAPYXxxN+ML+u3BACoUQf6/Cy9Z8YXUAtIfIWQn2m8Uv7BzogGAMAGt75XXTymZMJ8xhcTkAEAtrjJqzH1xcejfI0vAhIQZSS+QujAQE/BncFVLIo3AgBscjsKxjOQk+zqCACwyx3cTxn0kfITA+gfAVFG4iuE3BlfJqMZUsGDnSF2AIAFB3zsMizlB2IIRwAAW3oHZny5MaYY+aWOBCQgykh8hZA7jdd0xpe7tIQZXwAAG9yljqYDMW6ijJUlAABb/Owy3DCQJOvrzyqbdUpyXwAqj8RXCB3wZnwZjrAnKW4PALDH22XY59J7El8AAFv8zPgqnLHMqhggukh8hZCf0QypoMYXD3UAgAXeLsM+N1tJZ2PW7wkAUJuCzPiS8okzANFD4iuE3KUlpokvdnUEANjkdTKMl96z1BEAYI/jOL5mfNUl4qqL5wZh2NkRiC4SXyHkLS1JGha3r3OLNzKaAQAIzt1spcF06b1b3J5yKgAAC/qzjtwSXSa7OuaOpw4yEHUkvkLIKyZsPOOLhzoAwJ78Ziv+dhlmcB0AYENh/yZlOjkgyeQAIOpIfIXQAZ9LHd3RD6bxAgBs8GZ8+aw52Uc4AgBYUFiY3mSpo0Q5GKAWkPgKITdx5XfGF4UbAQA2uB0N83iUO569VgAANrgTA1J1ccViZhunpFgVA0Qeia8Qorg9AKAa5Gd8mf1zwh2NJxwBAGxwB2JMZ3vlznGXOhKUgKgi8RVCvovbM5oBALDIi0eGxe3dgZiME1MmS4V7AEAw7sB+ynBigFSwKoY+EhBZJL5CyNs+3m+NL5Y6AgAs8GYgG+6gVThww/J7AEBQ3mYrhhMDpHwMY8YXEF0kvkKorz+/ht1EfjSDhzoAILigAzHS4ILEAAD44c34MhyIkVgVA9QCEl8h5K1hN+1osFUvAMCiXp8j7Il4TMlErvgwOw0DAIIKMuPLTZax1BGILhJfIeM4jpf4qk8Yzvhyp/HSyQAAWJAvJmw+wl5fx07DAAA7bMz4YgYyEF0kvkKmL5N/IKdMd9FiGi8AwKIgu2g11LHTMADAjl6fpWCk/HJ9+khAdJH4CpnCkQjjGl9uJ4PRDACABW7NyXpfHY2BwRhiEgAgoF6fNScLz2FVDBBdJL5CpnBk3HipIzO+AAAWBZnxlWKpIwDAkiAzvtxz6CMB0UXiK2QKH+qxWMzoXHc0g2UlAAAbvJoqPkbYUyx1BABY4neXYSkfw1gVA0QXia+Q6bMwut6XySqTdazeFwCg9rh1J33FJG8WMh0NAEAwwWp8MeMLiDoSXyHjLSsJsH5dyifQAADwqzdIjS+WOgIALAm29H6gxhf9IyCySHyFTKAdtAoSX4xoAACCym8f72fGFx0NAIAdbt/GX3H7gYEY+kdAZJH4Chn3gexndD0RjymZyNUFO8AIOwAgoPxgjJ8aX+6MLxJfAIBgAk0OYMYXEHkkvkImSCej8DyKCQMAggpSd7LBi0cMxAAAggmy2Yo7S4wVMUB0kfgKmSCjGVJ+pphbkBgAAD8cxwm2fXySGV8AADsOBIlHdSx1BKKOxFfIBOlkSFJ9YiDxRUcDABBAf9aRu0Gwn1nIbnF7dnUEAARlY8YXAzFAdJH4Cpm+ALs6SvkZX+yiBQAIorCD4M7eMkE8AgDY4s74avC1AZg7EEM8AqKKxFfI2FrqyIgGACCIwpnD7mxiE4ywAwBssVPji3gERBWJr5Bx156z1BEAUEnuTK1kIqZ4PGZ8PksdAQC29AaY8eX2q9j1HoguEl8h446M1/tMfLnLUUh8AQCC8EbX/e4y7M34oqMBAAjmQIAZX+x6D0Qfia+QyS919FnjK8GujgCA4IIuvU8x4wsAYEmQDcDcCQVp+kdAZJH4CpnAuzrWMeMLABBcn6WakwzEAACCcgdRGnzM+HLjUX/WUdbdrhhApJD4Cpl84cZgI+wkvgAAQbgDMX6X3rszkNPEIwBAQEFmIRfGMQZjgGgi8RUygZc6MsIOALCAeAQAqBZecXs/M74KdiZmp2Egmkh8hUzgpSUDD3aKNwIAgvDikc8ZyMlEbidIEl8AgKDyG66YxyQ3HkmsigGiisRXyFir8UVHAwAQgLfUMRG05iT1VAAA/mWzjte38dNHisVi+eX39JGASCLxFTLe0hIf03ilfEeDabwAgCB6A8748nYZJh4BAAIoHND3XXeSOshApJH4Chmvo+FzhN2txcJDHQAQRH5Zic+BGEbXAQAWFMaRZNBZyMQkIJJIfIWMt9TR7wg7oxkAAAt6AywrkehkAADsKOzX+F5+zyxkINJIfIVMkMKNUsFDPZOxdk8AgNrTmx6o8RU0HtHJAAAEkM7kakXWxWOKx2OjHD28ZF3uPMrBANFE4itkrG0fz0MdABBAb8Bdht1OhtthAQDAD3epo9+BGInl90DUkfgKGTdh5ffBniLxBQCwIPBATIKljgCA4Nx45Le+lyTVUwcZiDQSXyETdESDXR0BADb0BZ3xVTC67jjM+gIA+OP2j4IlvpgcAEQZia+Q6Qv4YKemCgDABnezlaADMY4j9WdJfAEA/Ak6ECNJKWYhA5FG4itk8iMa/go3sosWAMCGwDOQCwZwGIwBAPgVtH8k5etOEo+AaPL1r9XVq1dr+vTpamho0Jw5c7Rly5aizrvtttsUi8W0aNEiPz8WyhcB9rtVr1uLhaWOAKoZcab6pftz8cjvDOTCDgrFhAFUArEmGvosFrdncgAQTcZPh3Xr1qmlpUUrVqzQQw89pBkzZmjBggXas2fPIc975pln9M///M9617ve5ftmIaUDFm9k/TqAakecCYegI+x1ibhiyiXPiEkAyo1YEx19Vorb00cCosz46XDDDTfo0ksv1dKlS3XSSSdpzZo1amxs1Nq1a0c8J5PJ6MILL9TKlSt17LHHBrrhWufV+ApYU4WHOoBqRZwJh6A1JyXJDWXMQgZQbsSa6HBXxLCrI4CR1Jkc3NfXp61bt2r58uXea/F4XPPnz9emTZtGPO9LX/qSjjrqKF1yySX67W9/6/9uEbzGF9N4AVSxcsWZ3t5e9fb2el93dnZKktLptNLptPF9u+f4OTesetO54vZxOb7brC4mpSXt7+1TOp20fIfRVIufNRtoN3NB26ya25o+TbQErTkp0UcCos4o8bV3715lMhk1NzcPer25uVnbt28f9pz7779fP/zhD/Xwww8X/XNsdkii9A+dTNaRu/FVLJv19Z4Syj3Me9OZQ54fpXYrJ9rNHG3mT1Q7JOWKM6tWrdLKlSuHvH7vvfeqsbHR6J4Ltba2+j43bF5oj0uKa/sfH9P6vY/6ukYinpAyUtt9/6Mph9m9v6irpc+aTbSbOb9t1tPTY/lO7ClHrGGApXx6enNtUhcf2j7FtltdPNfJ2t/r7/cTJXzW/KHd/AnSbibnGCW+THV1demiiy7S97//fU2cOLHo80rRIYnCP3T6MpL7K7uvrVWphPk1/rwvd43OfT1av379qMdHod0qgXYzR5v5E8UOiQm/cWb58uVqaWnxvu7s7NS0adN01llnqampyfg+0um0WltbdeaZZyqZrI2ZSz9/aav0yl80a+YMLZw5xfj8dDqtFVt/LUmac/o7dPLU8bZvMZJq8bNmA+1mLmibuYmeKPATaxhgKZ+te2KSEnr1L3tH7N+M1m4vPJcbzHlix5Nav3+H/ZsMIT5r/tBu/vhpN5P+jFHia+LEiUokEuro6Bj0ekdHhyZNmjTk+KeeekrPPPOMzj33XO+1bDY346iurk47duzQG9/4xiHn2eyQROkfOl0H0tKW+yRJ57z/bF/TeZ/s2KfrH31A8WS9Fi58z4jHRandyol2M0eb+RPVDkm54kwqlVIqlRryejKZDPQ5DHp+mPQPTEFO1ft/zwO7x8uJxWum3Wyppc+aTbSbOb9tVs3tXI5YwwBL+by65c/SU09o6uRJWrhw5qDvFdtuj2zYod+2P6vXH3OsFi54U4nvuLrxWfOHdvMnSLuZ9GeMEl/19fWaNWuW2travO17s9ms2tratGzZsiHHn3jiiXr00cHLHz7/+c+rq6tL3/zmNzVt2rRhf04pOiRR+IeO05tfc97YUK9YzLzO12Fj6iXlCjcW0x5RaLdKoN3M0Wb+RK1DUq44g+BsFBOmuD2ASihHrGGApXyyyvWJUsnEiG0zWrs11Oe6xf3Z6v03UrnxWfOHdvPHT7uZHG+81LGlpUVLlizRaaedptmzZ+vGG29Ud3e3li5dKkm6+OKLNXXqVK1atUoNDQ1661vfOuj8CRMmSNKQ1zG6fCcj5ivpJRXs6kjhRgBVijgTDvliwv7ikSS5+7S48Q0AyoVYEx12itsP7OpIHwmIJOPE1/nnn6+XXnpJ11xzjdrb2zVz5kxt2LDBKw65e/duxeP+HzoYWdrC1vHujiXpjKNs1lE87r/DAgClQJwJB5szvtg+HkC5EWuiw40h9QHiUXJgEId4BESTr+L2y5YtG3YasCRt3LjxkOfefPPNfn4klB+BCJT4KhgJ6ctk1RD3USEfAEqMOFP9bAzGuJPF6GgAqARiTTT0DQzEBJvx5U4OIB4BUcQwRohYmfF1UOILAAA/bMSkxMD28XQ0AAB+uYMnQeJRyi0Hw0AMEEkkvkIk3T8wmpHwvzyxcApwmgc7AMCntNfR8B+TmPEFAAjK5uQA4hEQTSS+QsRb6hhgGm8sFlPdQF0vdyt6AABM9dnc1ZEZXwAAn6wUt2cDMCDSSHyFiI3RDEmqS1C8EQAQjM0aX8xABgD4lS9uH2RVTK7ucS/xCIgkEl8hYivxlRzYoYYZXwAAv/ozwXfR8nZ1ZIQdAOBTn4UZX+6yfWpOAtFE4itEvGm8AUYzpPxSSR7sAAC/0u5Sx7rgNb6Y8QUA8MtGcXtqfAHRRuIrRPr6g9dTkeTV+CLxBQDww3GcfN3JQLs65v5kxhcAwC+K2wMYDYmvELG21DHhzvhiqSMAwFzhUnl3+bwf7OoIAAjK7dMEWeqYorg9EGkkvkIkbWFXRym/hr2fBzsAwIfCGcNBljoy4wsAEFS+uH2QGl8DEwMYiAEiicRXiFir8cWMLwBAAIXxI9iujrnrMOMLAOCXjeL2dQOzl9Ns/gVEEomvEOnLWKrxlaC4PQDAv8L44daN9MPb1ZHEFwDAJxvF7VkRA0Qbia8QSVt4qEv5GWP9WR7sAABz+RnIccViAZY6Dpzazwg7AMCnfB3kAAMxA/2rflbEAJFE4itEbBW3dx/s7i6RAACYSA/Ej7qAS+/d05mBDADwK21lqePArvdMDAAiicRXiOQf6sE6Gu6DnRlfAAA/+iwNxHgzvhhhBwD4ZLO4PfEIiCYSXyFiq8aXOxrCCDsAwA9bM5DzSx2JRwAAf9wNVwLN+PJKwThyHJJfQNSQ+AoRa0sd3am8jGgAAHxwR8SD7jKcX+pIPAIA+NNro7h9PH8udSeB6CHxFSK2itsn2dURABCAt9QxwOi6JLkbQmboZAAAfLIxOaCwZiXLHYHoIfEVIvldtIKNsLOGHQAQhO2ljgzEAAD86s8Er/FVmPiiwD0QPSS+QsRWjS93q186GgAAP9z44S6d9yvu1fhiIAYA4E96IIYkAkwOGLTUkckBQOSQ+AqRtKWlJXXeUkce6gAAcza2jpckdxynn4EYAIBP7nL5ZIDBmHg8lh+MISYBkUPiK0RsLS3JL3XkoQ4AMNfXb2cGcmLgT2Z8AQD8cBzHS3wlAs5C9iYHEJOAyCHxFSL5xFfQGl8sdQQA+NeftROP4t6MLzoZAABzhQMndfGAkwMGEmdMDgCih8RXiNgaYXeDAqMZAAA/7BW3z8UhCgkDAPwoHDipCzgYQzkYILpIfIWItaWOdQMzvvrpaAAAzKUHBmKC7KAl5Xd1ZMYXAMCP/oKBk6BLHd1ZzP0MxgCRQ+IrRKwtdRyY8UVNFQCAH33uro4B41GCQsIAgAAKB05srYphMAaIHhJfIeI+hG0Vt++jowEA8MHWDGRvBy0GYgAAPhTGj4ATvrzBHOogA9FD4itE3Gm3wXcsoXAjAMA/dyDG2lJHEl8AAB/cHR2TiZhisaBLHVkVA0QVia8QcR/sdQETX25HhWm8AAA/+qwVt8/9yeg6AMAPN34EnRgg5ftYxCQgekh8hYg7+mBrxhdLHQEAfqSt1/hiIAYAYC4/MSB4t7aOyQFAZJH4ChFbD/akt1UviS8AgLn80hI7M74yLCsBAPjgloIJOhAjsasjEGUkvkIkY2nGl/tQp6MBAPDDnYEcD1hPxQ1naToZAAAf+i2Vgim8RpoZX0DkkPgKEW/GV9ClJXEKNwIA/MvaikcDpzsOgzEAAHPuskSWOgI4FBJfIWJrhN0dzaCTAQDww1Y8KsybsfweAGDKVg1kiaWOQJSR+AoRW7s6uoGB0QwAgB+24lHh6cxCBgCY6vd2Gbax1NGtg0w8AqKGxFeIuKMPgXd1jDOaAQDwz018xYMOxBScnqGjAQAwVJIZX8xABiKHxFeIuM/goDVVvPXrjK4DAHywVUy48HQK3AMATFmt8eXO+KKPBEQOia8Qybjb9Vqa8UWNLwCAH1lLI+yxWMEsZGZ8AQAMuStYgk4MKLwGM76A6CHxFSL5qbzBfm3U+AIABGFzaYnb0aC4PQDAlK2ak5KUZFdHILJIfIWI+2BPsKsjAKCCso6deCTll5aw/B4AYMotRO+WcgnC7SOx9B6IHhJfIeKNsAecypvgoQ4ACMDqjC9vMIaYBAAwk7E6A5kZX0BUkfgKEVtTed1lJcz4AgD44db4sllThe3jAQCm3BpfSQvxiF0dgegi8RUSjuNYG9HwlpXQyQAA+OB2NOJWljpSdxIA4I8bO4LWQJbyMS3jEI+AqCHxFRKFk7OCzvhKUOMLABCAOxhuo5iwu7SE5fcAAFP9lna9L7wGNSeB6CHxFRL9BR2CuKWljjzUAQB+uPW4gsYjSUoyGAMA8Knf4q6Obh3lLPEIiBwSXyFR2CEIXOPLG81gdB0AYM5dlWilo+FuuEJNFQCAof6MvZqT7k7FTA4AoofEV0gUPoCD1vhy18BnqKcCAPDBnfFlYxct9xqMxQAATOVnfAXv1tbFmfEFRBWJr5AoTFIFfbCzfh0AEITN7eMpJgwA8MvdgdHGDOQ4fSQgskh8hUThAzjoc92dCkw9FQCAH17iy8aujl5MYsoXAMCMN+PLwlJHb8YXAzFA5JD4Cgn3AVwXjykWsKORoMYXACCAksz4IiQBAAzl41Hwbq0344tyMEDkkPgKCXc0w8YOWu4IfdaRHEY0AACGbCa+EuzqCADwyeZSxzriERBZJL5Cwq3xZXMHLYkHOwDAnFuPy2pxewZiAACGbC519DYAIx4BkeMr8bV69WpNnz5dDQ0NmjNnjrZs2TLisd///vf1rne9S4cffrgOP/xwzZ8//5DHY3j9FnfQKpw1xoMdQDUizlQ3dxmIlcTXwCUoJgyg3Ig14Zff1ZF4BGBkxomvdevWqaWlRStWrNBDDz2kGTNmaMGCBdqzZ8+wx2/cuFGLFy/Wfffdp02bNmnatGk666yz9Pzzzwe++VqSsfpQz1+DMl8Aqg1xpvplLc74irN9PIAKINZEQ34gJvhCpkQidw3iERA9xk+IG264QZdeeqmWLl2qk046SWvWrFFjY6PWrl077PG33HKLPvGJT2jmzJk68cQT9YMf/EDZbFZtbW2Bb76W9Fss3JhgxheAKkacqX79Fnd1TMSoqQKg/Ig10VC4AVhQbjxixhcQPUZZlL6+Pm3dulXz58/PXyAe1/z587Vp06airtHT06N0Oq0jjjjC7E5rXL6QcPBrxWPU+AJQnYgz4ZCxWlOFxBeA8iLWRIcbOyzkvbzkGTO+gOipMzl47969ymQyam5uHvR6c3Oztm/fXtQ1PvvZz2rKlCmDAs3Bent71dvb633d2dkpSUqn00qn0ya37B1vel616e3L3X8iFgv8Xgof5r19fUoP8ymISruVG+1mjjbzJ2i7VWt7hzHOuOcV/hl1mYFdtLKZTODPoNtX6evvr5n2C6LWPmu20G7mohpnpPLEGuJMefRnMrn/cZxh28ao3ZxcbEsHiG1RwGfNH9rNnyDtZnKOUeIrqGuvvVa33XabNm7cqIaGhhGPW7VqlVauXDnk9XvvvVeNjY2+fnZra6uv86rFri5JqlPvgf1av359oGvlZgTnfvX3tv5K45IjHxv2dqsU2s0cbeaP33br6emxfCfVoZJxRqqdz/G+noSkmDZvekAvPBrsWn/Zu0dSXP/7yKMat+cRG7dXE2rls2Yb7WaOODNUMbGGOFMezzwblxTXzp1/0voDO0Y8rph2e/SlmKSE2ve8FLi/FQV81vyh3fzx024mccYo8TVx4kQlEgl1dHQMer2jo0OTJk065LnXX3+9rr32Wv3qV7/SKaeccshjly9frpaWFu/rzs5Or4BkU1OTyS0rnU6rtbVVZ555ppLJQ2R4qtwfnnlFeuwPahp7mBYufGfg67VsvldZR3rPe9+no8alhnw/Ku1WbrSbOdrMn6Dt5o48V5swxhmp9j7HX3nsf6S+Xr37Xe/SmyeP83UNt80mT2rWIy+/pJPe8hYtnPN6y3caPbX2WbOFdjMX1TgjlSfWEGfK4/47H5f2PK8TTzhBC+cdO+T7Ju3mPNqun+x8REcc8TotXPj2Ut1y1eOz5g/t5k+QdjOJM0aJr/r6es2aNUttbW1atGiRJHlFHZctWzbiedddd52+8pWv6J577tFpp5026s9JpVJKpYYmY5LJpO8PUZBzq8JAUfu6RNzK+0jEY8pmHMUTiUNeL/TtViG0mznazB+/7VatbR3mOGPj/LBwa6qk6oO/37pEQpLkyE58qxW18lmzjXYzF7U4I5Un1hBnysMZWDBfVxe8T1OfzHWNs06MNhafNb9oN3/8tJvJ8cZLHVtaWrRkyRKddtppmj17tm688UZ1d3dr6dKlkqSLL75YU6dO1apVqyRJ//qv/6prrrlGt956q6ZPn6729nZJ0tixYzV27FjTH1+z8sXtLVRulFvg3qGYMICqQ5ypfu6OwDZikruLVpZdhgGUEbEmGtzYYWWXYXezFeIREDnGia/zzz9fL730kq655hq1t7dr5syZ2rBhg1cccvfu3YrH81sPfve731VfX58+9KEPDbrOihUr9MUvfjHY3dcQ24mvhLdriZXLAYA1xJnql8lYTHwN/CoZiAFQTsSaaHBzVHEbia+Ba/QTj4DI8VXcftmyZSNOA964ceOgr5955hk/PwIH8baOt5X4ijGiAaB6EWeqmxs7bMSkxEDHkngEoNyINeHn9pHiNuJRYqB/xMwAIHLiox+CatBve6mjO5WXEQ0AgKF+mx0Nd8ZXhngEADDjLnW00UXyJgaQ9wIih8RXSORnfNn5lXlLHRlhBwAYylqchRxnBjIAwKd84stePHKIR0DkkPgKCeszvmLM+AIA+OPN+LJYTDhLPAIAGHJXJdqYgexegrwXED0kvkLCXWtur7i9e12e7ACA4hUmqOzU+KKYMADAn4zFpY4xdhkGIovEV0j0Z+zVU5Hyo/Q82AEAJgoTVFZqfLHUEQDgk7ssMWFhBrJ7CfpHQPSQ+AoJ9/mbsJP3Kkh82bkeAKA2FHYIrNT4YqkjAMCnjMWl9/kaX4EvBaDKkPgKCUf2HupSfmkJSx0BACYKZ3zZWH7vjtKz1BEAYMoNHVZrfAW+EoBqQ+IrJNyHesxy4oupvAAAExnLiS93s2LCEQDAVJYaXwCKQOIrJNwHsKW8lxccmPEFADAxKPFlo6aK2D4eAOCP20eyMRBDjS8gukh8hYT7/LVU257t4wEAvriJr1iMpSUAgMoa2PjeyqoYrwZyNvClAFQZEl8h4Y6EuyPjQcXZRQsA4IOb+LIx20tihB0A4F/G4q6OtiYYAKg+JL5Cwu0OxC39xihuDwDwo39gKNzGbC+psKaKlcsBAGqIY7HGV5waX0BkkfgKCXdJoq0ZXxS3BwD44YaNOkuJL7aPBwD45Q7i2xqMkegfAVFE4isk3MevveL27owvO9cDANQGb7MVS9dzr0NxewCAqaxXB9lijS/CERA5JL5Cwn0A2yjcKLHUEQDgj2Oxk5G7zuDrAgBQLKtLHePuNYNfC0B1IfEVEjYf6oOvw5MdAFA8b8aXpXgUo6YKAMAnt7i9jaWObkkZZiAD0UPiKyRsj7BTTBgA4Ie3rMRacfvB1wUAoFgD+61YWuo4cE0SX0DkkPgKCds1VXiwAwD8yM9AtlzcnhnIAABDQWbi5AAAFW5JREFUbl8mYSEmMTEAiC4SXyGRL25vt6PBgx0AYMKrOWnpejFqfAEAfMrarPHlxSMCEhA1JL5CwnZNlfz28TzYAQDFc2dm2R+IIR4BAMy4G3VZqfHl9Y8CXwpAlSHxFRL5Gl92rhdjqSMAwId8PRU712PGFwDAL5t1kCkFA0QXia+QcLwaX3aL2/NcBwCYyFqu8eVehY4GAMCUu6tjwkKvllIwQHSR+AoJbzTD0m8szi5aAAAfbM9Azhe3BwDATL4cjI2ljrk/2WwFiB4SXyHhFROmpgoAoIJsdjJy18n9Sc1JAIApd/k9uzoCOBQSXyHhFRO2dD12LQEA+GF7sxWvo5G1cz0AQO2wufye/hEQXSS+QiJrsXCjxIgGAMAfN2zYikdxlpYAAHzyEl/U+AJwCCS+QsKxPMLOriUAAD8cb3TdzvXcTVvoaAAATGW8nYYt1viifwREDomvkLC5VW/hdehoAABM2J6BnF9aYuVyAIAa4lhc6shADBBdJL5CwvbMLG8XLXoaAAAD2aztGl+5P4lHAABTmYHYkbCy1DH//8QkIFpIfIWE7Zoq7mWyDGkAAAzY3mU4xi7DAACf8oMxNorb569BFwmIFhJfIZG1XVOFpY4AAB/cIvS24lG+uD0AAGbcMZOExRpfuesSlYAoIfEVFt4Iu53L0dEAAPhhu+YkNVUAAH5lbNb4YsYXEFkkvkIia/GhXngdRjMAACayjr1lJVJhcXviEQDAjNdHslzji+X3QLSQ+AoJb9TBcjFhHuoAABP5XR0tXdAbiLF0PQBAzchmc3/amBxQeA1iEhAtJL5CwvbSkjg1vgAAPtifgTz4ugAAFCvr7epoucYXBWGASCHxFRLe0hJL16OjAQDww/GWOtq5XpwZXwAAnzIWYxK7OgLRReIrZOzX+LJyOQBAjXC8zVZsFbfPYSAGAGDCcZyS7epITAKihcRXSOSXlti5ntthyTKcAQAwYLvGV8wrbm/negCA2lDYjbFe4ysb+HIAqgiJr5DwOgTWa6pYuRwAoEbYrvHlDsRQTwUAYKJwVlbcRo2vgv8nJgHRQuIrJOzP+Bp8XQAAiuFYjkcMxAAA/BiU+KLGF4BDIPEVEt6EL0vl7b0aX1auBgCoFW5nwHY8YiAGAGAiW7Ac0cYsZGp8AdFF4isk7I+wu8XteagDAIqXL25v53ruZQhHAAAThcmphI2ljoNmfBGUgCgh8RUS7oiGtY4GSx0BAD5Yr/EVZyAGAGAuUxA3bPWRvPwZIQmIFBJfIeEWWLS1fXx+aYmVywEAaoSX+LL0Lwj6GAAAPwp3XkzQRwJwCCS+QiK/fbztXR15qgMAiucQjwAAVSAzqLg9dScBjIzEV0jYrqmSr/Fl53oAgNrgdgasz0DOjnIgAAAFBu3qaKsQMoMxQCSR+AoJ28XtY15Hg4c6AKB4+V0dLRm4ENEIAGAia7l/JEnJgYv1Z4hKQJSQ+AoJ99Frb/v43J/kvQAAJthlGABQDdyZwjZ2dHSlkglJUm8/05CBKCHxFRL5pSV2rsf6dQCAH6Wq8UU4AgCYsL30XpJSdbnucW9/xto1AVQeia+QyNf4srR9vNfRoKcBACie7Y6GO5OZgRgAgInMwNIVWzs6SlIDM76ASCLxFRK217C7HRa6GQAAE/ldhu1cL0YhYQCAD47leCQVzPhKk/gCosRX4mv16tWaPn26GhoaNGfOHG3ZsuWQx//85z/XiSeeqIaGBp188slav369r5utZY7lYsJsHw+gmhFnqld+IMbyDGQrVwOA4hFrwi3jxiObNb5Y6ghEknHia926dWppadGKFSv00EMPacaMGVqwYIH27Nkz7PEPPPCAFi9erEsuuUTbtm3TokWLtGjRIj322GOBb76WOLL7YM/X+LJyOQCwhjhT3bzNVqwXt7dzPQAoBrEm/GwPxEhSqo6ljkAUGSe+brjhBl166aVaunSpTjrpJK1Zs0aNjY1au3btsMd/85vf1Nlnn61Pf/rTevOb36wvf/nLetvb3qZvf/vbgW++lri7ltiqqRKnxheAKkWcqW6O5Y4Gm60AqARiTfhls3ZLwUhSKsmMLyCK6kwO7uvr09atW7V8+XLvtXg8rvnz52vTpk3DnrNp0ya1tLQMem3BggW68847ze/Wh5179mn3PunR519TXZ3R260qr+7vk2RvqaObQHupq0+PPPfqkO/39/dHot3KjXYzR5v547Zbb39WyWSl78aeMMaZjs4Dev7laMSaYjz3yn5J9mZ8uZc5kM4MG48wGM9Mf2g3c26b7d3Xq8mHRyjQKHyxZn9fRk+88Bqf4YM8+5ceSVKiBEsdn9rTXbMxieelP7SbP/39/dqzv/Q/x+g3snfvXmUyGTU3Nw96vbm5Wdu3bx/2nPb29mGPb29vH/Hn9Pb2qre31/u6s7NTkpROp5VOp01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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -293,24 +198,13 @@ "print(norms.shape)\n", "#basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -338,24 +232,13 @@ "basis_functions, _ = linear_FEM_basis(x_plot, n)\n", "#basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -387,13 +270,13 @@ "basis_functions, _ = linear_FEM_basis(x_plot, n)\n", "#basis_functions = sawtooth_vector(x_plot, n)\n", "plot_basis_combinations(x_plot, basis_functions, n)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -410,13 +293,13 @@ " #Phi = sawtooth_vector(x, self.num_qubits)\n", " #Phi = Phi / torch.linalg.norm(Phi)\n", " qml.AmplitudeEmbedding(features=Phi, wires=self.wires, normalize=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -588,13 +471,13 @@ " self.wires[0],\n", " )\n", "\n" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "from torch import nn\n", @@ -609,13 +492,13 @@ " basis, norms = linear_FEM_basis(x, self.n)\n", " basis *= norms\n", " return torch.matmul(basis, self.coefficients)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -639,28 +522,13 @@ " print(\"norms: \", norms)\n", " \n", " return out" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0: ─╭QubitStateVector(M0)──Rot(2.42,0.96,6.12)─┤ <𝓗(-2.50)>\n", - "1: ─├QubitStateVector(M0)──Rot(1.07,3.21,4.06)─┤ \n", - "2: ─├QubitStateVector(M0)──Rot(1.74,5.69,4.00)─┤ \n", - "3: ─├QubitStateVector(M0)──Rot(4.42,4.18,4.04)─┤ \n", - "4: ─╰QubitStateVector(M0)──Rot(2.29,0.72,6.19)─┤ \n", - "torch.Size([2, 1])\n", - "tensor([[-2.2204e-16],\n", - " [ 1.4323e+00]], dtype=torch.float64, grad_fn=)\n" - ] - } - ], "source": [ "from qulearn import qlayer\n", "import pennylane as qml\n", @@ -760,31 +628,13 @@ "print(model(x))\n", "#model = LinearFEMModel(num_qubits)\n", "#model = CombinedModel(model1, model2)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([200, 1])\n" - ] - }, - { - "data": { - "image/png": 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78dNPPxV6LDU11XI/WFGioqIAoMjYzZkzB3v27MFnn32GGTNmoGPHjhg9enSR91cdOHAALVq0sAybNDtx4gRycnLQsWPHYttARJUXe7KIiJxEq9Vi1qxZGDFiBLp06YKBAwciMTER//nPfxAWFobXX3/dsm67du0AAK+++ip69uwJjUaDAQMGFPm8wcHBmDx5MmJjY9GrVy889thjOH36NBYuXIj77rvPquiDLWxpQ3EWLFiATp06ISIiAqNGjUKDBg2QmJiIvXv34sqVK1ZzDpVVixYtcP/992Py5MlISUlBYGAgVq9eXeIJdWmGDh2KL774AjExMYiPj0fnzp2RmZmJn3/+GWPGjMHjjz8Ob29vNG/eHGvWrEGTJk0QGBiIli1bomXLloWer3Xr1hg2bBg+++wzpKamokuXLoiPj8eKFSvQr18/PPTQQ3a39U6PP/443n77bej1evj7+wOApRCFt7c3Fi1aBAB46aWXsG7dOrz22muIjo5G7dq1AQDLly/HiBEjsGzZMgwfPtxp7bpTWWMSHByMCRMmYObMmXj00UfRu3dvHDp0CD/++GOhIXkTJ07Epk2b8Oijj2L48OFo164dMjMzcfToUXz77be4ePFikcP4ANnT2rJlS/z8889WRTtOnjyJd955B8OHD7dM8Lx8+XK0adMGY8aMwTfffGNZ12AwYOfOnRgzZkyh54+Li4OPjw+6d+/ucOyIqAJySU1DIqJyrLh5su5UVElxIYRYs2aNaNu2rfD09BSBgYFi8ODBlrLvZvn5+eKVV14RwcHBQqVSlamc+/z580V4eLjQarWiZs2aYvTo0Za5t8xsKeFeXBvMJdw//vjjQtugiBLn58+fF0OHDhUhISFCq9WKOnXqiEcffVR8++23pbahqBLu5ueMjo4Wnp6elvml4uLiiizh3qJFi0LbDxs2zKq8uBBCZGVlibffflvcc889QqvVipCQEPH000+L8+fPW9bZs2ePaNeundDpdFbvtah5sgwGg4iNjbU8X2hoqJg8eXKhOaqKe49dunQRXbp0KSVCQiQmJgoPDw+rudTMc7atW7fOat1Lly4Jf39/0bt3b8uyefPmCQBiy5YtJb6OeX9eu3at1fLi/h6K2tfKGhOj0ShiY2NFrVq1hLe3t+jatas4duyYqF+/vlUJdyGESE9PF5MnTxaNGjUSOp1OBAUFiY4dO4rZs2dblZovat+cM2eO8PX1FVlZWUIIuc/fd999om7duiI1NdVqXXNM16xZY1n2448/CgDi7NmzheIVGRkpnnvuuWKiSUSVnUqIu3BnMBEREdlt5MiROHPmDHbt2mXzts8++ywuXryI+Ph4BVpWvqWlpaFBgwb46KOPMHLkSJu379evH1QqFTZs2GC1/PDhw7j33ntx8OBBtGnTxkmtJaKKhEkWERFROXfp0iU0adIE27ZtwwMPPFDm7YQQqFmzJr788kv06NFDwRaWX7NmzcKyZctw4sSJEqdguNPJkycRERGBw4cPFxoyOmDAAJhMJquhhUREt2OSRURERERE5ESsLkhERERERORETLKIiIiIiIiciEkWERERERGREzHJIiIiIiIiciJORlwKk8mEa9euwc/PDyqVytXNISIiIiIiFxFCID09HbVr1y6xYimTrFJcu3YNoaGhrm4GERERERGVE5cvX0bdunWLfZxJVin8/PwAyED6+/u7tC0GgwFbt25Fjx49oNVqXdqWiojxVRbjqyzGV1mMr7IYX+UxxspifJVVnuKr1+sRGhpqyRGKwySrFOYhgv7+/uUiyfLx8YG/v7/Ld7CKiPFVFuOrLMZXWYyvshhf5THGymJ8lVUe41vabUQsfEFEREREROREbpdkLViwAGFhYfDy8kJkZCTi4+NLXH/u3Llo2rQpvL29ERoaitdffx05OTl3qbVERERERFTZuFWStWbNGsTExGDatGk4ePAgWrdujZ49eyIpKanI9VetWoVJkyZh2rRpOHnyJJYsWYI1a9bgrbfeusstJyIiIiKiysKt7smaM2cORo0ahREjRgAAFi9ejB9++AFLly7FpEmTCq2/Z88ePPDAAxg0aBAAICwsDAMHDsS+ffuc2i4hBPLz82E0Gp36vHcyGAzw8PBATk6O4q/lrjQaDTw8PFhun4iIiIhcxm2SrLy8PBw4cACTJ0+2LFOr1YiOjsbevXuL3KZjx4748ssvER8fjw4dOuCvv/7C5s2bMWTIkGJfJzc3F7m5uZbf9Xo9AJngGAyGQusbDAYkJiYiOzvb3rdWZkIIhISE4NKlS0wiSuDt7Y2aNWvafGOk+fMt6nMmxzG+ymJ8lcX4KovxVR5jrCzGV1nlKb5lbYNKCCEUbotTXLt2DXXq1MGePXsQFRVlWf7GG29g586dxfZOffrpp5gwYYKlt+lf//oXFi1aVOzrTJ8+HbGxsYWWr1q1Cj4+PoWW16xZE76+vggMDISHh9vkrBVWfn4+UlJSkJGRgcTERFc3h4iIiIgqkKysLAwaNAhpaWklVh6v0FnBjh078MEHH2DhwoWIjIzEuXPn8Nprr2HGjBl45513itxm8uTJiImJsfxuroXfo0ePQoHMzc3FpUuXUK9evSITMGczzzDt5+fHnqwS+Pv749KlS2jZsiU8PT3LvJ3BYEBcXBy6d+9ebsqDViSMr7IYX2UxvspifJXHGCuL8VVWeYqveZRbadwmyQoKCoJGoynUO5GYmIiQkJAit3nnnXcwZMgQvPDCCwCAiIgIZGZm4sUXX8Tbb78Ntbpw3Q9PT88iT8y1Wm2hD9VoNEKlUsHDw6PI53I2k8kEQNblvxuv567M92R5eHjY9YdY1GdNzsP4KovxVRbjqyzGV3mMsbIYX2WVh/iW9fXd5kxdp9OhXbt22LZtm2WZyWTCtm3brIYP3i4rK6tQMqLRaADIXiEiIiIiIiJnc5ueLACIiYnBsGHD0L59e3To0AFz585FZmampdrg0KFDUadOHcycORMA0LdvX8yZMwdt27a1DBd855130LdvX0uyRURERERE5ExulWT1798fycnJmDp1KhISEtCmTRts2bIFNWvWBABcunTJqudqypQpUKlUmDJlCq5evYrg4GD07dsX77//vqveQqXTtWtXtGnTBnPnzi3T+suXL8e4ceOQmpqqaLuIiIiIiJTiVkkWAIwdOxZjx44t8rEdO3ZY/e7h4YFp06Zh2rRpd6FlREREREREbnRPFhERERERkTtwu56s8kwIgWyDUbHnN5lMyM4zwiMvv1BBD2+txqay7l27dkVERAQ0Gg1WrFgBnU6H9957D4MGDcLYsWPx7bffombNmpg3bx4eeeQRAMDOnTsxceJE/PnnnwgMDMSwYcPw3nvvWeYHy8zMxOjRo7F+/Xr4+flhwoQJhV43NzcXb7/9Nr7++mukpqaiZcuWmDVrFrp27Wp/YIiIypkcgxFjVx3ClZtZrm6KWxBCQJ+uwcK/9nCKEoUwxspifJUlhEAQ1Ojt6obYgEmWE2UbjGg+9SeXvPaJd3vCR2fbx7lixQq88cYbiI+Px5o1azB69Ghs2LABTzzxBN566y38+9//xpAhQ3Dp0iXcvHkTvXv3xvDhw/HFF1/g1KlTGDVqFLy8vDB9+nQAwMSJE7Fz505s3LgRNWrUwFtvvYWDBw+iTZs2ltccO3YsTpw4gdWrV6N27drYsGEDevXqhaNHj6Jx48ZOjAgRkevsu5CCn09yQnTbqPBPVoarG1HBMcbKYnyVpKrq6hbYhklWJda6dWtMmTIFgJyE+cMPP0RQUBBGjRoFAJg6dSoWLVqEI0eO4H//+x9CQ0Mxf/58qFQqhIeH49q1a3jzzTcxdepUZGVlYcmSJfjyyy/RrVs3ADKJq1u3ruX1Ll26hGXLluHSpUuoXbs2AGDChAnYsmULli1bhg8++OAuR4CISBnJ6bkAgNZ1q2Jiz3AXt6b8yzfmI35fPDpEdoCHhqcmSmCMlcX4KivfmI/jB/e5uhk24V7gRN5aDU6821Ox5zeZTEjXp8PP36/I4YK2atWqleVnjUaD6tWrIyIiwrLMXLUxKSkJJ0+eRFRUlFUX+AMPPICMjAxcuXIFN2/eRF5eHiIjIy2PBwYGomnTppbfjx49CqPRiCZNmli1Izc3F9WrV7e5/URE5dX1DJlkNQz2RafGQS5uTflnMBiQdlrggYbVXT7RaEXFGCuL8VWWjK+rW2EbJllOpFKpbB6yZwuTyYR8nQY+Oo9CSZY97vwSUKlUVsvMCZXJZHL4tQAgIyMDGo0GBw4cKDRPma+vr1Neg4ioPDD3ZAX7ebq4JURE5ApMsqhMmjVrhnXr1kEIYUm+du/eDT8/P9StWxeBgYHQarXYt28f6tWrBwC4efMmzpw5gy5dugAA2rZtC6PRiKSkJHTu3Nll74WISGnmnqwgXyZZRESVEUu4U5mMGTMGly9fxiuvvIJTp05h48aNmDZtGmJiYqBWq+Hr64uRI0di4sSJ2L59O44dO4bhw4db9bg1adIEgwcPxtChQ7F+/XpcuHAB8fHxmDlzJn744QcXvjsiIucy92QF+elc3BIiInIF9mRRmdSpUwebN2/GxIkT0bp1awQGBmLkyJGWwhkA8PHHHyMjIwN9+/aFn58fxo8fj7S0NKvnWbZsGd577z2MHz8eV69eRVBQEO6//348+uijd/stEREpxtyTFezr5eKWEBGRKzDJqqR27NhRaNnFixcLLRNCWH7u0qUL4uPji31OX19frFy5EitXrrQsmzhxotU6Wq0WsbGxiI2NLfI5hg8fjuHDh5fceCKico49WURElRuHCxIRETmRwWjCzSwDACCY92QREVVKTLKIiIicKCUzDwCgUatQzYc9WURElRGTLCIiIicyDxWsXkUHtVpVytpERFQRMckiIiJyomSWbyciqvSYZBERETlRQdELJllERJUVkywiIiInKijfziSLiKiyYpJFRETkRCzfTkRETLKIiIic6HqGrC7IniwiosqLSRYREZETXb/VkxXMe7KIiCotJlmkqK5du2LcuHFlXn/58uUICAhQrD1EREpL5j1ZRESVHpMsKtf++ecfDBo0CE2aNIFarbYpYSMicgVz4QtWFyQiqryYZFG5lpubi+DgYEyZMgWtW7d2dXOIiEqUl29CapYBAOfJIiKqzJhkOZMQQF6msv8MWUUvF8Kmpnbt2hWvvPIKxo0bh2rVqqFmzZr4/PPPkZmZiREjRsDPzw+NGjXCjz/+aNlm586d6NChAzw9PVGrVi1MmjQJ+fn5lsczMzMxdOhQ+Pr6olatWvjkk08KvW5ubi4mTJiAOnXqoEqVKoiMjMSOHTuKbWdYWBj+85//YOjQoahatapN75GI6G67kSl7sTzUKgR4a13cGiIichUPVzegQjFkAR/UVuzp1QACinvwrWuAropNz7dixQq88cYbiI+Px5o1azB69Ghs2LABTzzxBN566y38+9//xpAhQ3Dp0iXcvHkTvXv3xvDhw/HFF1/g1KlTGDVqFLy8vDB9+nQAwMSJE7Fz505s3LgRNWrUwFtvvYWDBw+iTZs2ltccO3YsTpw4gdWrV6N27drYsGEDevXqhaNHj6Jx48Z2RIWIqPwwl2+v7quDWq1ycWuIiMhV2JNVibVu3RpTpkxB48aNMXnyZHh5eSEoKAijRo1C48aNMXXqVNy4cQNHjhzBwoULERoaivnz5yM8PBz9+vVDbGwsPvnkE5hMJmRkZGDJkiWYPXs2unXrhoiICKxYscKqp+vSpUtYtmwZ1q5di86dO6Nhw4aYMGECOnXqhGXLlrkwEkREzmGZiJj3YxERVWrsyXImrY/sUVKIyWSCPj0d/n5+UKvvyI+1PjY/X6tWrSw/azQaVK9eHREREZZlNWvWBAAkJSXh5MmTiIqKgkpVcGX2gQceQEZGBq5cuYKbN28iLy8PkZGRlscDAwPRtGlTy+9Hjx6F0WhEkyZNrNqRm5uL6tWr29x+IqLy5nq6nCOL92MREVVuTLKcSaWyecieTUwmQGuUr3FnkmUHrdb6fgGVSmW1zJxQmUwmh18LADIyMqDRaHDgwAFoNBqrx3x9fZ3yGkRErsTy7UREBDDJojJq1qwZ1q1bByGEJfnavXs3/Pz8ULduXQQGBkKr1WLfvn2oV68eAODmzZs4c+YMunTpAgBo27YtjEYjkpKS0LlzZ5e9FyIipZjvyWL5diKiyo33ZFGZjBkzBpcvX8Yrr7yCU6dOYePGjZg2bRpiYmKgVqvh6+uLkSNHYuLEidi+fTuOHTuG4cOHWw1rbNKkCQYPHoyhQ4di/fr1uHDhAuLj4zFz5kz88MMPxb724cOHcfjwYWRkZCA5ORmHDx/GiRMn7sbbJiKyibkni8MFiYgqN/ZkUZnUqVMHmzdvxsSJE9G6dWsEBgZi5MiRmDJlimWdjz/+GBkZGejbty/8/Pwwfvx4pKWlWT3PsmXL8N5772H8+PG4evUqgoKCcP/99+PRRx8t9rXbtm1r+fnAgQNYtWoV6tevj4sXLzr9fRIROeJ6OgtfEBERk6xKq6i5qYpKWsRt82916dIF8fHxxT6nr68vVq5ciZUrV1qWTZw40WodrVaL2NhYxMbGFvkcw4cPx/Dhw4ttAxFReVbQk6VzcUuIiMiVOFyQiIjIScw9WTXYk0VEVKkxySIiInKC3Hwj9DlybkDek0VEVLkxySIiInKC6xlyjiytRoWq3tpS1iYiooqMSRYREZETmIcKBvl6Wk3cTkRElY/bJVkLFixAWFgYvLy8EBkZWWIhBgBITU3Fyy+/jFq1asHT0xNNmjTB5s2bndomFmYoX/h5EJErJKezfDsREUluVV1wzZo1iImJweLFixEZGYm5c+eiZ8+eOH36NGrUqFFo/by8PHTv3h01atTAt99+izp16uDvv/9GQECAU9qj1crhIFlZWfD29nbKc5LjsrKyABR8PkREd8P1DJZvJyIiya2SrDlz5mDUqFEYMWIEAGDx4sX44YcfsHTpUkyaNKnQ+kuXLkVKSgr27NljOeEOCwtzWns0Gg0CAgKQlJQEAPDx8VF0iIjJZEJeXh5ycnKsJvklSQiBrKwsJCUlISAgABqNxtVNIqJKpKAni+XbiYgqO7dJsvLy8nDgwAFMnjzZskytViM6Ohp79+4tcptNmzYhKioKL7/8MjZu3Ijg4GAMGjQIb775ZrEn4Lm5ucjNzbX8rtfrAQAGgwEGg6HQ+tWrV4fRaERiYqIjb69MhBDIycmBl5cXx/uXwN/fH9WrVy/y8yqJeX1bt6OyYXyVxfgqqyzxTdJnAwCq+2j5OdiI+6/yGGNlMb7KKk/xLWsb3CbJun79OoxGI2rWrGm1vGbNmjh16lSR2/z111/Yvn07Bg8ejM2bN+PcuXMYM2YMDAYDpk2bVuQ2M2fOLHKi3K1bt8LHx6fY9qlUKvaclANGo9Hhe7Li4uKc1BoqCuOrLMZXWSXF9+g5NQA1Ei+dw+bNZ+9eoyoQ7r/KY4yVxfgqqzzE13xbSmncJsmyh8lkQo0aNfDZZ59Bo9GgXbt2uHr1Kj7++ONik6zJkycjJibG8rter0doaCh69OgBf3//u9X0IhkMBsTFxaF79+6830gBjK+yGF9lMb7KKkt8V16LB26k4sEObdE7IuQut9C9cf9VHmOsLMZXWeUpvuZRbqVxmyQrKCgIGo2m0LC8xMREhIQUfTCrVasWtFqtVQ9Ts2bNkJCQgLy8POh0hcfNe3p6wtOz8E3LWq3W5R+qWXlqS0XE+CqL8VUW46uskuKbli0nIq7h783PwE7cf5XHGCuL8VVWeYhvWV/fbaon6HQ6tGvXDtu2bbMsM5lM2LZtG6Kioorc5oEHHsC5c+dgMpksy86cOYNatWoVmWARERHZS58jx+n7cyJiIqJKz22SLACIiYnB559/jhUrVuDkyZMYPXo0MjMzLdUGhw4dalUYY/To0UhJScFrr72GM2fO4IcffsAHH3yAl19+2VVvgYiIKqi0bJlkVWWSRURU6bnNcEEA6N+/P5KTkzF16lQkJCSgTZs22LJli6UYxqVLl6xKm4eGhuKnn37C66+/jlatWqFOnTp47bXX8Oabb7rqLRARUQWUm29EjkGOmvD3YpJFRFTZuVWSBQBjx47F2LFji3xsx44dhZZFRUXh999/V7hVRERUmelv3Y+lUgF+Xm53aCUiIidzq+GCRERE5ZF5qKCfpwfUas5jSERU2THJIiIicpA5yWLRCyIiAphkEREROcxcWZBFL4iICGCSRURE5DA9KwsSEdFtmGQRERE5yJxksbIgEREBTLKIiIgcxjmyiIjodkyyiIiIHFRQ+ILl24mIiEkWERGRw8zzZLEni4iIACZZREREDuNwQSIiuh2TLCIiIgdxniwiIrodkywiIiIHmefJYpJFREQAkywiIiKHcbggERHdjkkWERGRgzhPFhER3Y5JFhERkQNMJoH0XFYXJCKiAkyyiIiIHJCekw8h5M+cJ4uIiAAmWURERA4xF73w0qrh6aFxcWuIiKg8YJJFRETkABa9ICKiOzHJIiIickAai14QEdEdmGQRERE5QM+eLCIiugOTLCIiIgdwuCAREd2JSRYREZEDzIUv/JlkERHRLUyyiIiIHMCeLCIiuhOTLCIiIgcUFL7gHFlERCQxySIiInKAPjsfAIcLEhFRASZZREREDuBwQSIiuhOTLCIiIgdYhgsyySIioluYZBERETnAXF2QPVlERGTGJIuIiMgBnIyYiIjuxCSLiIjITkIIFr4gIqJCmGQRERHZKcdgQp7RBIA9WUREVIBJFhERkZ3MRS80ahWq6DQubg0REZUXTLKIiIjsZC564e/lAZVK5eLWEBFRecEki4iIyE6cI4uIiIrCJIuIiMhOaVmcI4uIiApjkkVERGQnzpFFRERFcbska8GCBQgLC4OXlxciIyMRHx9fpu1Wr14NlUqFfv36KdtAIiKqNMzDBdmTRUREt3OrJGvNmjWIiYnBtGnTcPDgQbRu3Ro9e/ZEUlJSidtdvHgREyZMQOfOne9SS4mIqDKwzJHlxSSLiIgKuFWSNWfOHIwaNQojRoxA8+bNsXjxYvj4+GDp0qXFbmM0GjF48GDExsaiQYMGd7G1RERU0bHwBRERFcXD1Q0oq7y8PBw4cACTJ0+2LFOr1YiOjsbevXuL3e7dd99FjRo1MHLkSOzatavU18nNzUVubq7ld71eDwAwGAwwGAwOvAPHmV/f1e2oqBhfZTG+ymJ8lVVcfG9myeOFr07N2DuA+6/yGGNlMb7KKk/xLWsb3CbJun79OoxGI2rWrGm1vGbNmjh16lSR2/z2229YsmQJDh8+XObXmTlzJmJjYwst37p1K3x8fGxqs1Li4uJc3YQKjfFVFuOrLMZXWXfG99xFNQA1Lp0/hc0ZJ13TqAqE+6/yGGNlMb7KKg/xzcrKKtN6bpNk2So9PR1DhgzB559/jqCgoDJvN3nyZMTExFh+1+v1CA0NRY8ePeDv769EU8vMYDAgLi4O3bt3h1bLoSnOxvgqi/FVFuOrrOLi++U/+4GbN/FA+7boHRHiwha6N+6/ymOMlcX4Kqs8xdc8yq00bpNkBQUFQaPRIDEx0Wp5YmIiQkIKH9jOnz+Pixcvom/fvpZlJpMJAODh4YHTp0+jYcOGhbbz9PSEp6dnoeVardblH6pZeWpLRcT4KovxVRbjq6w745ueIwtfVPP1YtydgPuv8hhjZTG+yioP8S3r67tN4QudTod27dph27ZtlmUmkwnbtm1DVFRUofXDw8Nx9OhRHD582PLvsccew0MPPYTDhw8jNDT0bjafiIgqID0LXxARURHcpicLAGJiYjBs2DC0b98eHTp0wNy5c5GZmYkRI0YAAIYOHYo6depg5syZ8PLyQsuWLa22DwgIAIBCy4mIiOzB6oJERFQUt0qy+vfvj+TkZEydOhUJCQlo06YNtmzZYimGcenSJajVbtM5R0REbizfaEJmnhEAJyMmIiJrbpVkAcDYsWMxduzYIh/bsWNHidsuX77c+Q0iIqJKSX/rfiwA8Pdyu8MpEREpiN0+REREdjAPFayi08BDw8MpEREV4FGBiIjIDix6QURExWGSRUREZAdzTxbvxyIiojsxySIiIrIDkywiIioOkywiIiI76HM4XJCIiIrGJIuIiMgOnCOLiIiKwySLiIjIDvpsWcLd34tJFhERWWOSRUREZAf2ZBERUXGYZBEREdlBbyl8wYmIiYjIGpMsIiIiO7DwBRERFYdJFhERkR04XJCIiIrDJIuIiMgOnCeLiIiKwySLiIjIDnr2ZBERUTGYZBEREdlICAF9jizhziSLiIjuxCSLiIjIRpl5RhhNAgDnySIiosKYZBEREdnIfD+WTqOGl5aHUiIissYjAxERkY3SsgrmyFKpVC5uDRERlTdMsoiIiGxkniOLlQWJiKgoTLKIiIhsxDmyiIioJEyyiIiIbGSZI4tFL4iIqAhMsoiIiGzEObKIiKgkTLKIiIhsxCSLiIhKwiSLiIjIRuaJiP29PVzcEiIiKo+YZBEREdmIhS+IiKgkTLKIiIhsxMIXRERUEiZZRERENuI9WUREVBImWURERDbicEEiIioJkywiIiIbWYYLMskiIqIiMMkiIiKykT6HPVlERFQ8JllEREQ2yM03IsdgAsCeLCIiKhqTLCIiIhvos+UcWSoV4OfJebKIiKgwJllEREQ2MN+P5efpAbVa5eLWEBFRecQki4iIyAYsekFERKVhkkVERGQDFr0gIqLSMMkiIiKyASciJiKi0rhdkrVgwQKEhYXBy8sLkZGRiI+PL3bdzz//HJ07d0a1atVQrVo1REdHl7g+ERFRaSzDBb2YZBERUdHcKslas2YNYmJiMG3aNBw8eBCtW7dGz549kZSUVOT6O3bswMCBA/HLL79g7969CA0NRY8ePXD16tW73HIiIqoo2JNFRESlcaska86cORg1ahRGjBiB5s2bY/HixfDx8cHSpUuLXP+rr77CmDFj0KZNG4SHh+O///0vTCYTtm3bdpdbTkREFYW5J6uqD5MsIiIqmttM8JGXl4cDBw5g8uTJlmVqtRrR0dHYu3dvmZ4jKysLBoMBgYGBxa6Tm5uL3Nxcy+96vR4AYDAYYDAY7Gy9c5hf39XtqKgYX2UxvspifJV1e3xTs/IAAFW0asbbSbj/Ko8xVhbjq6zyFN+ytkElhBAKt8Uprl27hjp16mDPnj2IioqyLH/jjTewc+dO7Nu3r9TnGDNmDH766SccP34cXl5eRa4zffp0xMbGFlq+atUq+Pj42P8GiIioQlhyWo0jKWo8fY8RnUPc4hBKREROkpWVhUGDBiEtLQ3+/v7Fruc2PVmO+vDDD7F69Wrs2LGj2AQLACZPnoyYmBjL73q93nIvV0mBvBsMBgPi4uLQvXt3aLUcpuJsjK+yGF9lMb7Kuj2+XyccBlJuIqpdG/RuXcvVTasQuP8qjzFWFuOrrPIUX/Mot9K4TZIVFBQEjUaDxMREq+WJiYkICQkpcdvZs2fjww8/xM8//4xWrVqVuK6npyc8PT0LLddqtS7/UM3KU1sqIsZXWYyvshhfZWm1WqTnGgEAgX5ejLWTcf9VHmOsLMZXWeUhvmV9fbcpfKHT6dCuXTurohXmIha3Dx+800cffYQZM2Zgy5YtaN++/d1oKhERVWBprC5IRESlcJueLACIiYnBsGHD0L59e3To0AFz585FZmYmRowYAQAYOnQo6tSpg5kzZwIAZs2ahalTp2LVqlUICwtDQkICAMDX1xe+vr4uex9EROS+OE8WERGVxq2SrP79+yM5ORlTp05FQkIC2rRpgy1btqBmzZoAgEuXLkGtLuicW7RoEfLy8vD0009bPc+0adMwffr0u9l0IiKqAEwmgYzcfADsySIiouK5VZIFAGPHjsXYsWOLfGzHjh1Wv1+8eFH5BhERUaWRnpsPc01ef2+3O4QSEdFd4jb3ZBEREbmaPkcOFfTSquHpoXFxa4iIqLxikkVERFRG+mwOFSQiotIxySIiIiojFr0gIqKyYJJFRERURvoc9mQREVHpmGQRERGVkZ5zZBERURkwySIiIiqjtFuFL/yZZBERUQmYZBEREZVROgtfEBFRGTDJIiIiKiNLT5YX58giIqLiMckiIiIqI3MJdw4XJCKikjDJIiIiKiPzZMQcLkhERCVhkkVERFRGaezJIiKiMmCSRUREVEbp7MkiIqIycOjOXYPBgISEBGRlZSE4OBiBgYHOahcREVG5k8bqgkREVAY292Slp6dj0aJF6NKlC/z9/REWFoZmzZohODgY9evXx6hRo7B//34l2kpEROQyQhTck8XhgkREVBKbkqw5c+YgLCwMy5YtQ3R0NL777jscPnwYZ86cwd69ezFt2jTk5+ejR48e6NWrF86ePatUu4mIiO4qgwkwGAUA9mQREVHJbBouuH//fvz6669o0aJFkY936NABzz//PBYvXoxly5Zh165daNy4sVMaSkRE5EpZcqQgNGoVqug0rm0MERGVazYlWV9//XWZ1vP09MS//vUvuxpERERUHmUb5f/+Xh5QqVSubQwREZVrdlcXTE5OLvaxo0eP2vu0RERE5ZK5J4tDBYmIqDR2J1kRERH44YcfCi2fPXs2OnTo4FCjiIiIypvsfNl7xaIXdsjPBa6fBW6cB25eBNITZCURIqIKyu4S7jExMXjqqacwYsQIzJkzBykpKRg6dCiOHj2KVatWObONRERELmceLsieLBuZTMD/PQgkn7JeXvteoOskoHEP17SLiEhBdvdkvfHGG9i7dy927dqFVq1aoVWrVvD09MSRI0fwxBNPOLONRERELmceLsieLBslHb+VYKkAnR+g9QFUauDaQWDVs8DnD0F1Ls7VraxYbl4Erh5wdSuIKjWHJiNu1KgRWrZsiXXr1gEA+vfvj5CQEKc0jIiIqDyxJFleFSjJEgJIPAbk6IGwB5R5jb92yP8bdwcGr5U/ZyQDez4F9v8XuHYIHmsGon3AfUBGe6BaHWXa4WpZKbeGTJ4FjHlA26GAxqHTsKLl5wFLewHp/wAPTwE6TwBYqIXorrP7r3v37t147rnnEBgYiCNHjmD37t145ZVXsHnzZixevBjVqlVzZjuJiIhcKtsoT1Tdfrhgfi5w6nvgzFbg/HYgM0kuH7gGaNrL+a93/hf5f4OuBct8g4EeM4COrwK750L8vgh1UvdDfPYA0GsW0OrZipEY6K8BB1YAh74E9FesH1NpgHbDnP+aZ3+SCRYAbH8PSLsC9P7E+a9DRCWye7jgww8/jP79++P3339Hs2bN8MILL+DQoUO4dOkSIiIinNlGopKZTMD294HNbwAXfgWM+a5uERFVQNmW4YIK9D7cDdk3gV1zgLmtgG+fB46sLkiwAODAcue/Zn4u8Pce+XODhwo/7hsM9Hwf+c/HIdW7HlTZN4ENLwLfDAFy0pzfnrvl8n5gzRDg3y2BnR8WJFj+dYHqjeTPp75X5rUPfSn/r90WgEp+rqsHAXmZyrweERXJ7iPF1q1b0aVLF6tlDRs2xO7du/H+++873DCiMjv8JfDrR/Ln+P8DfIKAZo8C7UcCtVq5tm0VxbltQNw0edX1vhcqxhVmIhtlu2sJ95w0mVzFfw4Ybp1o+9UGWj0DNIoGvAOBxQ8AZ7cC6YmAX03nvfbleCA/G6hSA6jRrPj1Qlrh16bT0SfgL2h2fQSc/B+QeALovxKo2cJ57VFa0ilg27vA6duqL9d/ALhvJNC4J+DpK9/Xoijgr50y8dFVcd7rpycAZ2/d3/bEZ8D1M8C6kcDZn+Cxog+8g0Y477WogBCA0eDqVlA5Y3dP1p0JluUJ1Wq88847djeIKgCjQR5oTEblXysrRZ78A/JA5l0NyLour9z9X2dg5ZOyd6uilgrOSpEJ0O+LgdRLyryGEEDcVCDxKLB5grxRPaP4efKIKqqsfDcbLmjMl/c8fdoW2D1XJlg1WgD9FgOv/Ql0fxe450EgpCVQ9z5AGIGj3zi3DX/dNlSwlIszQuUB0wPjgOe3AFVDgZTzwOfdgD/XOLdNSshIBjaOlcnT6R9kYY+2zwGj9wIjNgMtn5IJFiCTzYB6gDG34H41Z/lztfwcQyOB4CbyguOw74EqwVAlHUOX09OguvCrc1+zsjOZgJVPwOOjUDxw9n2od/8buHZYLifnSE+AZmVfhN74za3O52xKslavXl3mdS9fvozdu3fb3CByU9fPAnvmA189A8wKAxZGAmuHKf/H8PN0IDsFqNEcGLoRmHAWGLJBHtBUauD8NmBFX2BJD+DSPmXbcjcY8+X9DRvHAv9pDXx0D/Dlk8CWN4FvhioT73/+lDfGq7WAxlNe7V4UBZz5yfmvRVSOZd26buQWhS8u/S57p34YD2TdAIKaAANXA6N3A20GAh466/XbDJL/H17l3O8RcxLRsIihgsWp0w54cSfQ8GHZC7bhReCXmeXz5EoIGbMF9wGHVgLCBIQ/Coz5HXh8AVCzeeFtVCqgySPy59M/Orct5qGCbZ8rWB56H/DiDphqtYGnMQOar58G9i4on/F0RwdXAH/9ApUpH0EZp6HZ8T7wWRdgfntgzzx5MbSiMxmV3Z8OrYT60l7Uv/GLW42ksSnJWrRoEZo1a4aPPvoIJ0+eLPR4WloaNm/ejEGDBuHee+/FjRs3nNZQKoeyUuTwk88fll8mW9+WJ+B5GfLxk/+TV0+Vcnk/cPAL+XOfOYBGK/81fBh4einwykE5tM3DC7gSDyztAXwzDEi5oFyblJJ4HPhhAjAnHFjZTx7Mb16UjwU2kO/x2qGCq8bOdPgr+X+zvsCLv8iENjNZ9mitHQ7o/3H+axKVQ24xXDAvC/jpbVldLvmUHArYezYweg/Q9JHiT1BaPCkvoiSdAP457Jy2ZN+U30uAddGLsqhSHRj8LdB5vPx954fADzF3Z4REWaVckN/H342W77VmBDAyDhjwFRDctORtzQVGzvzkvB6Py/GycqHWB2hxx1Q6VevCOOR/uBTYCSphAn56C1j/ImDIds5rlwe5GfIi5Pb3gS8el0U/lO5NykiWF3sBGB8Yjz/rDoOpcS9AW0X2xG6dAnwSDqx/SY7wqSiSzwD7PgO+exlY3Al4r4a8oK3E36fJCByQ53p/V7fhYk05YNM9WTt37sSmTZswb948TJ48GVWqVEHNmjXh5eWFmzdvIiEhAUFBQRg+fDiOHTuGmjWdOK6byo+EY/Iq2LFvZRlaQFZJatBFJjj3dAGu7JcHxG3vyptvbT3AlsaYL58fAmgzGKgfVXidwHuAPp8AD04EfnkfOLgSOPEdcHoz0ClGHrzvvJpbnhjzgTM/Avv+D7i4q2C5dyDQ/HE5DKROOzlE8sdJwL5F8r6Lhg87rw2GHODIreFDbQfLeyNG/QJsnwH8vhA4vgE4+7MsE9xhFKDWOO+1K7OT3wPfvw741pA9EMFNgVqtgbDOBUOO6K4r90nW5Xhgw7/kyR0gvxt7vi+/I0rjHSC/U46tkz0ztds63p4Lu2TPTlBTwL+27durNUC3qXLbHyYAfyyVvXJPfg54eDrePkcc/06OKMhLlxe5uk4CosbKC31lUb+TnDMsM0kmonXbOd6mQyvl/y2eADz9Cj+u9caheqNQp31vaOLekUNDk0/JpDCgnuOv7woZycDJTfLY/vcewHRb4au/dgApf8nhsUod6+PeAXJSgZAImB6ciItbtqJ5795Qm3LlOdL+JUDCEVlk5sgaeezu8oZ73WcIyF6qqwdkrE/9ANw4V3idi7vkd8e9Q5z72ue3A2mXILwCcLVaB7hTaT2bC1889thjeOyxx3D9+nX89ttv+Pvvv5GdnY2goCC0bdsWbdu2hVpt961eVF4JIb+wdv/HurckJAJoPQiIeFqeEN6+/OpBWZTi25HAS78CVZ0498kft764vKoC0bElr+sXAjw2D+jwkuxt+2uHvCp6cpMczlHnXue1yxmM+fLgt3NWQW+VSiNPgNoOlcnsnQfyjmPlvRcXd8lhkfUindOW05vlAcS/TkFlMK2XPHFr9axMBK4ekMMV/1wFPPpvmfhVREYDkJ0qr1jnpcsePa23818n+ybwv9fkvYWZSXKopplGB9TvCDTqDjR/zH1PjEoiBJD6txye6szvDAcZjCbkmmQvULmbjFgI+fe/ZZI8yfSrBfT9FGjSw7bnaTNIJllH1wI93nM8kTEPFXT0Itt9LwA+1WXPy4mNsrdu4NdlT2icKT9PnljvWyx/rxcljyPVG9r2PB46oNHD8v2c+dHxJCsvU170AqyHCt5JpYLpvhehqdVKDulPOAJ81hV4epk8triD/Fw5UubQylv3XN/WW1U1VH5HVg2VI2mOrZPfqc+udP4Fqgu/An9+DUAFPDoXUN92Su3pC7QbDtw7TJ4L7Z5bkAye+E72HHePLf/f4RlJ8j0e+lIWUTFTa4GwTvJezlqtgYSj8rxq27tAi35FJ/n2+mMZAMDUqj9MhnJ8YbwIdlcXDAoKQr9+/ZzYFCq3rvwhu8PNvSkqtbwaEzUWqNu+6G1UKqDPbPkFnnBE3i804kfnXE1KuyL/kAGg2zRZArgsQloCQ76TB6LNE+WwmP92Ax54DXjobcfb5SiTUR4QdnxYcCXaOxBoPwJo/zxQtW7x21atC7QeIA86v80BBjnpRnHzUMHWAwv3UtVqLYfGHFgO/Bwr7936vJs8Ier2jkyA3ZUhR/bGXj0AXDsIXD0EpN1RWKRBV7k/OXt8+Pb3ZIIV1EQWJrh+Bkg+Dfy9Wybdf+2Q/7a+LU/wIp6RV661Tjyo3U0ZycDfv8mr0P8ckX+XuXr5WKcY2UtaDnpI03MKrpD7e5WjEu75ubIgjXnodIsn5Amfd4Dtz9XgIVl1MP2avFeoRT/H2nZ70QtHtXhCfh9+PQA4Fyff86Nz7+79GemJwOqB8nsBAB4YBzz8jv0TCjftLZOs01vkfu6Iw6vkUP3ABvJ7oTT3dJb3va0ZLL+7v7jVw/LgG8pMkOwMN/+WF1gPfSl7NM1qtwWa95MXngIbFCyvHwWsGSp7Qlb0BZ5bB/gEOqct+XnyfkdAHp/rtgcMRVQXVKlkAt1/pRz2/+vHshf0+Hp5EfOB1+Q/Z1aYdIarB+Vk4Sc2yUIqgByG2vQRec9ho2jAy79g/cY95MWZlPNyRE30NOe0I+2qvAgBwNR2GBBfRA9aOVZO/5KoXEj5C9j6TsFcHhod0G4EEPUyUK1+6dtrvYFnv5BXya7+If9gH5zgWJuEkAlSXoasntTOxnK0KhXQ8klZUevHN2V3/m//lolkv88da5sjrh2WvULXDsrffarLL977Xij7l2+n12VSdGaLvKoU4mCnetpVeXACCm6Kv5NaI0sTN+srx54fWQPs/1xeqXt4CtB2SLk4QS6VEEDSSXnydv4X4NJeID+n6HW9qsqx/3/tkAl7yyed146rB+XwEkAOdb3nQXlQM7fxxjlZnvn0ZuDib7Kdl/YCWyZD07wfAnLDndcWpRjzgcu/A6c2y/0rufD9vVBrAZNBXjD45zDw1BLnnRwVR4gST9j1OfIEqopOAw9NORmtkXlDJh1X4gGogOjp8nvD3sRDrZEXa36bAxxYJi+m2ftcN/+WxxCVRl7xdoYGXYCn/gusHiwv7lS7B+g0zjnPXZrM68AXj8nhdV4BwBP/5/jEzY17yIuWiUdldVh7ezVu/m25LwiR/yr7ZxYQCjz/kzymHlopR09c2CVjXI56kZF0Sh6nj64tOOH3qwXcO1ReAAy8p+jtGkUDw/4HfPW0PLZumQQ8+Zlz2vTLe/ICWJVgOaS1LGq2AJ5ZDnQ+Kof4//2bjPmhL4HHPpXtdSUh5PFl939k28zq3id7R1s8aZ1Y3c5DJ3u/Vw+Ut5O0GwZUC3O8TeZiMvUfkBceUYGTrGrVqkFVxj/elJRKUE2lojKZ5HxTP8fKyk4qtRwS2HWS/FK2ReA9wCMfyepQOz+SV5uCGtnftpOb5AmmWgv0/Q9g79DUKkHA00vkla/vxgAXd8FjaTSq1nrR/rbZIydN9lzs/6/8IvH0lycNHV6yfWhD9YYyvsfXywPS00sda9ufXxd8uZU2FMa3hjx4tRks75W7cU4OeYv/XA4tdPY9ec4ghDzwntgkh56Yew/NfEPksMva98ohpTWay3tb1BpZ6Wznh/IiRJOezrkKaTLeujIqZO/UPQ9aP65SAUGN5b+oMTIJPrZO3jOXeBTqo2vQBYBp6Uag4yvyyn95SXCN+TIpPfatvAiQfdP68Zot5Yl4nfbyRCSosbzau+kVmYh91gUYsMrxCweW9hjkSelfOwqGgBrzZILRY0aR9zDpb92QVW6GCppMwPpRMsHyqgo8tRRo7ISTtLbPyQtif+2QF01aD7DvecxV8+q2L/7EzB7hfYBeM+UJ88/T5AW/O4s8OFtWCvBFP5lg+dUGhn9v+/DAovgEyouFl/bKAhgdRtn+HCYTsPHlWxce75cX5myh9QYeny+/o/83Dri0R1al7Leo4AKPq9w4Lz/jk/8rWNagK9DhRTnnWFl63Oq2kwVUlkTL/TniWcf/Tk79IBMRQBbdsrXXOCRC7kMnNwE/TZGjJL58Sr6v6FhA5+NY++xx5Q95ofTSXvm72gNo+bS8FaGs37tNH5H35F/YKafWeXaFY20y5hf00Nt6Qb2csCnJmjt3rkLNoHLj+jn5hX35d/l7WGdZmaqGA1fIWz0rv9zOb5Mn3sP+Z19ylJ0KbH5D/tzp9ZIntiyr5o/Lm7JXD4Iq5Tw6p8+AOFUfiOjn+HOX5ubfwPI+QNpl+XvEM0CP9x2bCLRzjEyyjm+QX9j17rfveYz5cvgJIBOnsmrQRc4Ls/9zeYUu8ZgchtLkEeBhJw0fcFR6gpxL5tCXshKXmUYnDxANH5blpoPDi78i3GmcjE/aJZnQOjrUB5BlgK8dlIl2j/dKX79qHeCBV2VCdfUgTPv+D+LYemj+OSwnH935kRz+48pkK/GEjPOxb4GMxILl3tWAJr1kghr2oKwkd6dWz8i/8TWD5TDJFY/J4amOXKQBZDK7/kX5d3KnQyvlyW7vjwv14qTd6smqWl6GCv6+UH6nengBwzfL4dDOUL0h0GWSvFK/eaK8v8XWHpbL8fLkGJBDi5zt/tGysl/8/8mqbVVDix+67qicNDlNRuJROaHysP85J8Eya9JLntie3GTfRO/xn8mh/FofoN9C+//WI56Ww+6+fV72Hn89ALh/jOwdvdtFRvIygV2fyPLn5uJazfrK4cP23ENdtx0QORr4fYEcMTJmr/33Z6X8BWwYLX++/2V5odYeKpX8jmnUXf6txH8m//21Q/Yk1mpt3/PaKvWyfP1j6+TvHl5yP7x/dMm3KBRFpQJ6fiDnKD3xHXDuZ8d6587FAfqrt4p9PQa44YwDNh0thg0bplQ7qDw4/4schmHIBHS+8n6QdiPs7y0yU6mAR+cAC6NkF/ShlbIr2VY/TwcyEoDqjQrK+jpDjXBg1HaYvh0JzfmfIb57EagaYn+CUhbpCTL5SLssu9QfnWvbPDLFCYmQJ9XHNwBfPQsM/5/tX9Z5mcDaEbJnR+cnDwS28NDJIaWtBwI7Zsrhb2d+hMe5OLSs/jCQHQVoa5T+PM6WcEz2Pp3abD3GvElPeQBv3KPsN+tqvYGe78l7DXd/KhPR4oaslMXpLcDWW0NOHnpbFmspq1tj/o01F+BnPIge1S5DE78YuH5aJlu/fiyHNtVuY3/7bGEukrNnnkwCzLwD5fx1LfrJK+5luQod0hJ4cYecVPzaQXmyOzLO/gsR5qv+x9fL3vA+n8i/Ge8Aea/nD+PlEKC1w+TJb5c3LIVczD1ZfuWhJ+vaoYLhYT0/cF6CZdbpdTkdx5V4eUJpy4WxG+eBVf3lcNsmveSJuhJ6zZRD7M78CKx5Tu4ntvzdlEVWipyq4tohOYR72CbHk/w7NesrP8sLv8r7cR+aXPZtr58tSGZ7zHA8+aveEBi5VY5i+X2BTOT/3i2LYjgzsSzJmZ/k36H54mOjaHnRydGLqg+9JXvE0i7JasO9Ztr+HIZs+Z2fmyZ7ILuXUnSrLHQ+8qJOk56yHPr1M8CyPnJfU7oo17XD8js16wYAlbwt4KG3HRsqGtJSnjf+sQRYMwQYvNa+4cJ5WfLYCsh2eXgWfc9bOefQ2fP58+cxZcoUDBw4EElJSQCAH3/8EcePH3dK44qyYMEChIWFwcvLC5GRkYiPjy9x/bVr1yI8PBxeXl6IiIjA5s2bFWubWzv1gzyYGDJladkxe+W9Ns6qFFktTH7JAbIyU3piiatbycuUV58OyAoz6PsfWeHOmbwDYHz2K/xT9V6ojLnA1wPlyYISslKAlU8ANy8AAfWBEVuck2CZPb5Q3vicmyZfJ/lM6duYZV6XNwif/Ule0Xrqc/uv+PkEyoPHmL1A4x5QmfLRMHkrPBbeJ0/8c9Pte15bJZ+W86MtfkAeZIVRHiD7fgqMPy3HyLd8yvZqSM0ekz1fxlw5L5E9jPlyWMXX/WXFwvqdbB/uc5s8Dz+YOk8Axh2VvWteAXKI05Luck4TpScfPf+LvIq5sp9MsFRqeRI5cDUw4YwshhPWybYb672rAYO+kfffpP4NrHrGvn1HCOD7cXIYrEoDPLNMXuypc6+8Wf6eB4GXdskb/9Uecljj5w/LE54zW6HPzgVQDnqycjNkxVaTQfYStX/e+a+h8QCe/D8518/fvwF755dtu4xkOewpO0X2ijy9VLkiCmqN/H4Kbgak/yMTrfxc5z1/2lVg2SOyAI5XgCxy44zRE3eq3hB4ZJb8eeeHBcPQSnPzorwYlp8jC5a0H+mc9nh4Ar0+AAaukX97//wJLOooe8adGd87mUzALx/I85C0y7J3sv9XcqifM+Lu6Sur3wKyMuSVA7Ztn6OXtxYkHJUJ99PLnFvdslG0PFaGdZbHgi+flCMBlHJxtzzWZ92QF2L/tUv2hDrjXryeHwANuwGGLHmx9+89tm2ffEYWJbu0R44wUeI77i6x+wx6586diIiIwL59+7B+/XpkZMgJaP/8809Mm6bMsKA1a9YgJiYG06ZNw8GDB9G6dWv07NnTkuDdac+ePRg4cCBGjhyJQ4cOoV+/fujXrx+OHTtW5PrlnerPVaiVut/5kwf+uUZecTDmyROiIeuVKSsaORqo1UYOv/jiMXnFqrSTvit/AIs7y/lRAKDrZOfdRH0ntQYH6o+GqVZbeZLw1dPyxnJnSk+Qz5t0Qt64O3Qj4F/Lua+h85HVBWu1ll+gXzxetoTx6kFgSQ9ZOcu7mrx67Ywx+cFNgcFrkT9wLfRedaDKSZX3oc1tJasQ3Xl/jrMkHJNDXxbeL4cuAPLG3TG/y6u17YY5dq+ISiVPjlQa4PQPwJdPF0y8WhaJx+XfgXnC7g4vAUM2OOek1Mtfzg/36iGgaR/5t/3jROCbIc7fpwGZnK9/USZXCUdlD2GHl+Tr9/9S7keOnJD4Bt+qDBYkT/q+GVb2CzUmk5x3bEl3OSRTpZb3DzbrW3hdrRfw8Nty4t7Wg2Sy9fdvwKpn8MTORzDFYyUicEb5ZLW493F5P7DuBdnL7FdbTk2hVHW9wAYFV/u3z5Dfwfl5xa9/4Vf5+d+8II8fg75RvmKap5+c48mrasHcjM74bJJPy+9C8z1Yz28BarVy/HmLE/mSrJQLAHFT5b2sxRFC3qey6AE5hNErQN5T5ez9oGkv4F+75T1Q+Tmy92dhlBwC5uz9P0cvhwXvvJVsdngJeDleTlvizPfVOFrekyVM8rvw8NelT55rMsmh4fPa3RpirFKuMIhPoJyaoE57eVxc2U+Zi71ntsokLlcvL+wN+95597sC8nt0wCo59N6QKY+NF3aVvp0Q8h7jz7rKc6QqNeT3/t3qRVWASgj7/lqioqLwzDPPICYmBn5+fvjzzz/RoEEDxMfH48knn8SVK1ec3VZERkbivvvuw/z58qqayWRCaGgoXnnlFUyaNKnQ+v3790dmZia+//57y7L7778fbdq0weLFi8v0mnq9HlWrVkVaWhr8/Z14866tTEaIT5pClZkMoasCVdPeclhYzRayuk1ZDmb5ufJkKDNZFiZIPC7vmTm7VT7eepA8aCtZvjXhGLDi0YIT63od5ZCc4KbyfWi0cozwX7/IG97N5UP9agNPLFK0gILBYMDmzZvR+8H20K7oJYeihLSSY9WDm8lhhb41yzY+PT9PXl1NT5Bjii/Hy5tBk25dmfIOlCXtHbnXrTSZN+SV2Oun5Yllw4flsLamveV7MBrkzdInNspKXf8clttVrScT7aDGTm2OwWDA5h++R5/62fD47ZOCyQxVGtmz1KSn7E3wC5FXCm25D8BouLVvJwH6a8CBFZayrwDkFf+uk50/rAqQw+LiphUMQQx/VMY5uKnsqdR4yINHbrps25kfgSNrgaRbPf46P+DxeQ7fvG/Zf3v3hlZ7W0IjhLxyu/Ud2fuh9pBxbv64HCJp/rsr8wvlyLnT0q4C+ityyNLe+bf+plXyXsCuk5SpBnjlgPz+MGTJ/aZxdxnrsE7yRNt8P0pWivxb++dPOceK+d47jafsCW8zsGyvl3ZVTvJ9YEVBWXlAFkUJvU8OJazTTn4vaHRyn81KkUOsLu6SSZF3ABDaQQ6RrHOvXNcroORRAsZ8+f1x86L8l3BUVnnVX721gkpeBLmns03hs5kQsofIXGE2oD7Q5U2ZoAqTnJMr8bjs5TBXI/OuBjy/FQhuYtNLFbv/lsW5bfLilTDJeYnCOsvXr96o5GOjEHJfyroh7/FKOimrXR7/Tu7jQU2A59bbXvDJXtvfk738gOwlb/64jLV3oExek04Ah76SIw0AOWKh30LrsuUlsCvGQsj7dX56q+C+Sr9aQKNusvelVhv5fe3pV3pCJIS8SJx9U+7LN87L48CJjfJvVOMJ9J1bfDVbZ8i8Dnz2UMGUHNUbywtSoffJ8wytlzxXSjgqE/dj6+T/ABDYUI7OaNStyKd2aB++XVYKsPxReYyoWg9oP1zeIxzUVN4n5eFZtljnZcpjYkayjO/VA/JfwlH5t9KklxzJocR8j4D8rL8eUDBfXp12QKsBcuSI+T5cIeQ0P8e/kxdDU/6Sy8M6y6qytw0Nd1p8naCsuYHdSZavry+OHj2Ke+65xyrJunjxIsLDw5GTU0z5Yzvl5eXBx8cH3377rdX8XMOGDUNqaio2btxYaJt69eohJiYG48aNsyybNm0avvvuO/z5559Fvk5ubi5ycwu6xPV6PUJDQ3H9+nXXJlm56cDOj2A4/A18DIWvRAsPb3mSodHKf2qtPAAa8+QXhiETqrzMYp/e2P4FmHp8IE/GlZadCvXe/0C9/3OobiuTLaACvPyhykmzWt3U4kkYe35k37wvNjAYDIiLi0P37t2hTbsAjxWPFGoLAAgPL1mcQFdFftGp1ABUt+KcBeRlQpVffG+jqVYbGB/55O7c2Kr/B5r/jYH6YsFVJKFSQ3X75I3m5RodRHhfGLvFOv/eBtwRX40KquProfl9PlTmxPPO9uh8ZY+Ih2fBPg0hDw7CdCve2YAhu8h4C6ggmj8OY9Rrzr1KV5SUv6DZ9TFUx76F6ra7c4VGJ6/GZadAZciybp9aC9G4B4wPvSNPBh1kFd8iDkCqa4eg+XE8VAlHCj0mtD7yJMnDSyYqag+5X5vy5Qm/KV9WGs1Nh8pYdG+GqNESxt5zIBS+j0D1926of5kB9dU/rF8fKvkdofaAKjPZ+jGvqjDd+zxM970gkxxb5efgq1XLEXBxM3prD0Nnyip9mxIIlUaemGp9bvsOEXIoYF5GoX3Fsp2uCkSjHjC1HQoRpnCCZZafC/WhFVDvngtVZtGjRgC5r5vaDIGp4zi7eudL239Lo/59ATTbCo+iERqd3Ld1fnLfFibZe2HMA3JSrY5BtzPVbgdj/6+VnzrAqrEC6l9mQLP304JFKjWg1sph7OZlGh1MXd+CqcNomwpdOBTjHD3Uv34I9aGVRX/fqrUywdboCs5DhLj1HWKQw6pz0or//vANgfHpLxT//gAA5GVA/cdSqH+fB9UdIymEd6D8G7ytnUJbBaZO42Hq8FKJF/8c3YetZCTC44tHobp5odBDQq2VoxV0vvLzN5+DCKM8Jubn3DouFn8eboroD2OfucpP6G3IguaH16E68R1U5guRuPUdKH+yOh8RHl4w3T8Wps4TC+3bTo2vg/R6PYKCgpRLsurWrYtvvvkGHTt2tEqyNmzYgAkTJuD8eed2cV67dg116tTBnj17EBVVMNHeG2+8gZ07d2Lfvn2FttHpdFixYgUGDiy4arlw4ULExsYiMbHooSbTp09HbGzhmxlXrVoFHx8XlNW8kxColvUXat/chxD9IXjnpUAjyn4zoAlq5Hn4IcszGHqvUOi96yLVpwFu+jS4u5M6AvDKS0HThO9QU/8nPA1pUMNkaePNKg2R7NcCSf6tcbOKa7qKq+QkoM7N3+GfcwV+OVfhm5sA9W1fEqUxqjyQow1AjrYa9F51cd2vBa77NUOeh433/jhBldxEhN7YhXopv8HbYD29QrpnLfwd1BWXAzu5pG3eucmoqf8TIfrDqJr1N3T56ZZ9wRbmfTvXwx83qzTEuRqPINPLyUMxS+GbcxWNkragatYF+OYkwENYn1Dkq71w0+ceXK12P64FdIDB4+5PQFkl5x/UTv0DtVP3IyD7ol3PIaBCjjYA2brqyNYG4oZvOC4GdYVQ3b37lXxzrqHejV2oe3NvoX0aADJ1QdB7heK6XzNcqt4F+RrHrtYuO6PG4Rtq9K+fjb7+5xGQ+ReqZZ1HQNZFaI1ZUIt8qE0GmNQeSKnSBNd9w3HDtyl0xkwEZpxFYOYZ+GdfgdZUtuHeJpUGWbpgZOpqINOzBpL8I5Ds1wImtRMmdLeDxpiLe67/jEZJm+GZX3BPXL7aE5cDO+FMzb7I0d3FhOROQqB26j4Ep5+Eb841+OVchacxo0ybGlUeyNYGIt27LvRedaH3DkVC1bYui7VPbhJqp+5H7dT9qJYlr+znq3VIv9W288E9ke5tY+U3J1Gb8lA94wxq6I+gRvpR+ORdh4fJtnu1TNAgV1sVGZ4hyPAKQYZnLVypFoU87d29kO1hzEaD5J9QN2UvvPNuWH1f53r44aZPQ9ys0giXAju5ZN/WGfSof2Mn/HKuynOQnH8KHVNKk6/2RK6HP7J0QUj1uQc3fRogtUoDZOuCFGp10TwNaahz83eEpuwudNzJV+mQVLU1rgbchyT/1g5/V98NWVlZGDRoUKlJlt1HxAEDBuDNN9/E2rVroVKpYDKZsHv3bkyYMAFDhw6192ldbvLkyYiJibH8bu7J6tGjh2t7slCQxbfv9y9ota8AAExCwGTIlF3gOWlQmfLlkCBjnrzyr/GE8NDJ7mDv6oBXVWhUKvgB8APg+ukGnwMAGIUJxqwUOZTRvw78vfzhD+BupldFXyUpuOHSaDLCmJsuhw3lpN262izk1TphAjy8ZI+AzkdeYfIOhE6lgg6APwDXHBJvNwIwGWHISJJD2G5dcfTS+qApgKYKv3pZr0IZhYAxVy+H8Biy5RVcY568GqpS3/qnkvu21kfu27oqgHc1aFRq+ADwgSv3bTnXjRAmGPRXocpIhPCpLnu0dFUQACAAQAsnv6ptV/nkTfIGo0H2kufqgVw9VEaDvPJsypdXRdUe8ntErSno7dL5Aboq8FBrLN8jNQAoUBKgDF4oeB/ZN+W//GwgsCF0nn4IAhAEwBmDctck7gdu3ETbtvcisl3hipsCgBGQF8JUKlQDUNSAW0N+rhwOlHVd7tvmnhUAwtOvIMZeVeGp1sATQCCAuzRgrRRPyO8QY96tfUNeRa8Lx7/fnHOVuo/1c+boZRGBHD1UuXq5T6tkT61Qe8ieF59AQFsFnioVPCH3l/JhOADAoP9H9gIF1IOvSg1fALXtfEYlegIEAIMhW97HnH1T9gCZ8uVIA5VKHmfUHrJH0StA9jZrq8BDpbJ8FwLO+Ru1z1MAACEEDDmpcpiu1hvqgDBUV6lQHUBZxxko09NSME+dECYYcjMK9um8DLlPC9OtydTVEFpv2UOu9ZbDTHVVoAOgg4x1mJNaZR/Z4WHISikozQ8A3gEI9vBCcClbl7eerLKwO8n64IMP8PLLLyM0NBRGoxHNmzeH0WjEoEGDMGWKE+aMuUNQUBA0Gk2hHqjExESEhBQ9tCkkJMSm9QHA09MTnp6Fu4O1Wq3LP1SzQm3R6YAqhSfPdDu6WkDA3e15KErxn7UW8PQCSv0qKM+0gKcCRU1saUFZ/pZ0QYBf+TndsVtQA/nvLrLpu0qrBbx8ADgwN1t5YH4f1ZRLrdNzZSJUzdfLsWOBVgt4+wLVXft3aD8tACdXd7392Z15rNVWB1DEHGzuRIH9xOnnM1ot4OMPV5/CO0xXA/B3fHoRRc8XdZ5w+326qmPHm/JwPl7W17f7BhydTofPP/8c58+fx/fff48vv/wSp06dwsqVK6HROH/iS51Oh3bt2mHbtoJ5V0wmE7Zt22Y1fPB2UVFRVusDQFxcXLHrExERFSUtWw7L9nd1CXciInILDh8t6tWrh3r17s4VuZiYGAwbNgzt27dHhw4dMHfuXGRmZmLEiBEAgKFDh6JOnTqYOVOWnX3ttdfQpUsXfPLJJ+jTpw9Wr16NP/74A5999tldaS8REVUM6TlyMuKqXuVjRAMREZVvNiVZt9+rVJo5c+bY3JjS9O/fH8nJyZg6dSoSEhLQpk0bbNmyBTVryq7HS5cuQX1bWdyOHTti1apVmDJlCt566y00btwY3333HVq2VKCMMxERVUhCCOhvJVn+3uzJIiKi0tl0tDh0yHqizYMHDyI/Px9Nm8pb5s+cOQONRoN27do5r4V3GDt2LMaOHVvkYzt27Ci07JlnnsEzzzyjWHuIiKhiy8wzwmiShXj92ZNFRERlYFOS9csvv1h+njNnDvz8/LBixQpUqyaLLty8eRMjRoxA5853af4OIiIihZnvx9KoBLy0d2EuQSIicnt2Hy0++eQTzJw505JgAUC1atXw3nvv4ZNPPnFK44iIiFwtLUsmWd4egOouzydIRETuye4kS6/XIzk5udDy5ORkpKenF7EFERGR+9HnyCTLx/mFc4mIqIKyO8l64oknMGLECKxfvx5XrlzBlStXsG7dOowcORJPPvmkM9tIRETkMubhgj6seUFERGVk9yFj8eLFmDBhAgYNGgSDQR6APDw8MHLkSHz88cdOayAREZErmZMsbw/h4pYQEZG7sDvJ8vHxwcKFC/Hxxx/j/PnzAICGDRuiSpUqTmscERGRq+nNSRaHCxIRURk5PPihSpUqCAwMtPxMRERUkeg5XJCIiGxk9z1ZJpMJ7777LqpWrYr69eujfv36CAgIwIwZM2AymZzZRiIiIpcxT0TMeYiJiKis7D5kvP3221iyZAk+/PBDPPDAAwCA3377DdOnT0dOTg7ef/99pzWSiIjIVQoKX/CeLCIiKhu7k6wVK1bgv//9Lx577DHLslatWqFOnToYM2YMkywiIqoQ0nhPFhER2cju4YIpKSkIDw8vtDw8PBwpKSkONYqIiKi8sBS+4HBBIiIqI7uTrNatW2P+/PmFls+fPx+tW7d2qFFERETlBefJIiIiW9l9yPjoo4/Qp08f/Pzzz4iKigIA7N27F5cuXcKPP/7otAYSERG5UsFwQd6TRUREZWN3T1aXLl1w+vRpPPnkk0hNTUVqaiqefPJJnDlzBp07d3ZmG4mIiFxGn8OeLCIiso1Dh4zq1avjsccew/33328p2/7HH38AgFVBDCIiIneUm29EjkEe33hPFhERlZXdh4wtW7Zg6NChuHHjBoSwHkKhUqlgNBodbhwREZEr6bPlHFkqFeDF6oJERFRGdg8XfOWVV/DMM8/g2rVrMJlMVv+YYBERUUVgvh/Lz9MDapWLG0NERG7D7iQrMTERMTExqFmzpjPbQ0REVG6Ykyx/L44VJCKisrM7yXr66aexY8cOJzaFiIiofDEXvfD31rq4JURE5E7svjQ3f/58PPPMM9i1axciIiKg1VofgF599VWHG0dERORK5omIqzLJIiIiG9idZH399dfYunUrvLy8sGPHDqhUBYPVVSoVkywiInJ7lnuyOFyQiIhsYPdR4+2330ZsbCwmTZoEtdruUYdERETlFnuyiIjIHnZnR3l5eejfvz8TLCIiqrBY+IKIiOxhd4Y0bNgwrFmzxpltISIiKlfM82T5e7Eni4iIys7uS3NGoxEfffQRfvrpJ7Rq1apQ4Ys5c+Y43DgiIiJXSrMMF/QAslzcGCIicht2J1lHjx5F27ZtAQDHjh2zeuz2IhhERETuqqDwBXuyiIio7OxOsn755RdntoOIiKjcMc+TVdXbA5kubgsREbkPVq0gIiIqhqXwBasLEhGRDZhkERERFaOguiCTLCIiKjsmWUREREUwmQQycmV1wareLOFORERlxySLiIioCOk5+RBC/szCF0REZAsmWUREREUwF73w0qrh6cHDJRERlR2PGkREREUomCOLvVhERGQbJllERERFYNELIiKyF5MsIiKiIujZk0VERHZymyQrJSUFgwcPhr+/PwICAjBy5EhkZGSUuP4rr7yCpk2bwtvbG/Xq1cOrr76KtLS0u9hqIiJyVxwuSERE9nKbJGvw4ME4fvw44uLi8P333+PXX3/Fiy++WOz6165dw7Vr1zB79mwcO3YMy5cvx5YtWzBy5Mi72GoiInJXnIiYiIjs5RYTf5w8eRJbtmzB/v370b59ewDAvHnz0Lt3b8yePRu1a9cutE3Lli2xbt06y+8NGzbE+++/j+eeew75+fnw8HCLt05ERC5iri7IniwiIrKVW2Qae/fuRUBAgCXBAoDo6Gio1Wrs27cPTzzxRJmeJy0tDf7+/iUmWLm5ucjNzbX8rtfrAQAGgwEGg8HOd+Ac5td3dTsqKsZXWYyvshhf57uZKY8Fvjo146swxld5jLGyGF9llaf4lrUNbpFkJSQkoEaNGlbLPDw8EBgYiISEhDI9x/Xr1zFjxowShxgCwMyZMxEbG1to+datW+Hj41P2RisoLi7O1U2o0BhfZTG+ymJ8nefUeTUANa5cOIu43DMAGF+lMb7KY4yVxfgqqzzENysrq0zruTTJmjRpEmbNmlXiOidPnnT4dfR6Pfr06YPmzZtj+vTpJa47efJkxMTEWG0bGhqKHj16wN/f3+G2OMJgMCAuLg7du3eHVsvhK87G+CqL8VUW4+t8664fAG7cwP33tkL3iBqMr4K4/yqPMVYW46us8hRf8yi30rg0yRo/fjyGDx9e4joNGjRASEgIkpKSrJbn5+cjJSUFISEhJW6fnp6OXr16wc/PDxs2bCj1g/H09ISnp2eh5Vqt1uUfqll5aktFxPgqi/FVFuPrPPocIwCgmq+XJaaMr7IYX+UxxspifJVVHuJb1td3aZIVHByM4ODgUteLiopCamoqDhw4gHbt2gEAtm/fDpPJhMjIyGK30+v16NmzJzw9PbFp0yZ4eXk5re1ERFSxsfAFERHZyy1KuDdr1gy9evXCqFGjEB8fj927d2Ps2LEYMGCApbLg1atXER4ejvj4eAAywerRowcyMzOxZMkS6PV6JCQkICEhAUaj0ZVvh4iI3AAnIyYiInu5ReELAPjqq68wduxYdOvWDWq1Gk899RQ+/fRTy+MGgwGnT5+23Ix28OBB7Nu3DwDQqFEjq+e6cOECwsLC7lrbiYjIvQghOE8WERHZzW2SrMDAQKxatarYx8PCwiCEsPzetWtXq9+JiIjKKsdggsEojyGyJ4vHEyIiKju3GC5IRER0N5l7sTRqFaroNC5uDRERuRsmWURERHcwF73w9/KASqVycWuIiMjdMMkiIiK6QxqLXhARkQOYZBEREd0hLYtFL4iIyH5MsoiIiO7AObKIiMgRTLKIiIjuwPLtRETkCCZZREREd7AkWV5MsoiIyHZMsoiIiO6gz84HwOGCRERkHyZZREREd2B1QSIicgSTLCIiojtY5sny9nBxS4iIyB0xySIiIroDe7KIiMgRTLKIiIjuoGfhCyIicgCTLCIiojvo2ZNFREQOYJJFRER0Bw4XJCIiRzDJIiIiuo3BaEJmnhEAJyMmIiL7MMkiIiK6TXpOvuVnfy9WFyQiItsxySIiIrqNeaigr6cHPDQ8TBIRke149CAiIrpNQWVB9mIREZF9mGQRERHdxtyTxfuxiIjIXkyyiIiIbsMki4iIHMUki4iI6Db6HJZvJyIixzDJIiIiug3nyCIiIkcxySIiIrqNZbigF5MsIiKyD5MsIiKi2+iz5TxZ7MkiIiJ7MckiIiK6jd4yXJAl3ImIyD5MsoiIiG5jLnzB6oJERGQvJllERES3YeELIiJyFJMsIiKi23CeLCIichSTLCIiotvo2ZNFREQOYpJFRER0ixAC+hxWFyQiIscwySIiIrolIzcfRpMAwHmyiIjIfkyyiIiIbjH3Yuk0anhpeYgkIiL78AhCRER0S1pWQdELlUrl4tYQEZG7YpJFRER0S8EcWZyImIiI7Mcki4iI6BbOkUVERM7AJIuIiOgWyxxZLHpBREQOcJskKyUlBYMHD4a/vz8CAgIwcuRIZGRklGlbIQQeeeQRqFQqfPfdd8o2lIiI3BbnyCIiImdwmyRr8ODBOH78OOLi4vD999/j119/xYsvvlimbefOncsbmImIqFRMsoiIyBnc4s7ekydPYsuWLdi/fz/at28PAJg3bx569+6N2bNno3bt2sVue/jwYXzyySf4448/UKtWrVJfKzc3F7m5uZbf9Xo9AMBgMMBgMDj4Thxjfn1Xt6OiYnyVxfgqi/F1jpuZ8vvfV6e2iiXjqyzGV3mMsbIYX2WVp/iWtQ0qIYRQuC0OW7p0KcaPH4+bN29aluXn58PLywtr167FE088UeR2WVlZaN++PWbOnInHH38cKpUKGzZsQL9+/Yp9renTpyM2NrbQ8lWrVsHHx8fh90JEROXXyrNq/HFdjcfrG/Fw7XJ/eCQiorssKysLgwYNQlpaGvz9/Ytdzy16shISElCjRg2rZR4eHggMDERCQkKx273++uvo2LEjHn/88TK/1uTJkxETE2P5Xa/XIzQ0FD169CgxkHeDwWBAXFwcunfvDq2WQ1mcjfFVFuOrLMbXOTasPAhcv47IthHo3a6uZTnjqyzGV3mMsbIYX2WVp/iaR7mVxqVJ1qRJkzBr1qwS1zl58qRdz71p0yZs374dhw4dsmk7T09PeHp6Flqu1Wpd/qGalae2VESMr7IYX2Uxvo7JyDUCAKpV8Soyjoyvshhf5THGymJ8lVUe4lvW13dpkjV+/HgMHz68xHUaNGiAkJAQJCUlWS3Pz89HSkoKQkJCitxu+/btOH/+PAICAqyWP/XUU+jcuTN27NjhQMuJiKgi4jxZRETkDC5NsoKDgxEcHFzqelFRUUhNTcWBAwfQrl07ADKJMplMiIyMLHKbSZMm4YUXXrBaFhERgX//+9/o27ev440nIqIKxzJPFpMsIiJygFvck9WsWTP06tULo0aNwuLFi2EwGDB27FgMGDDAUlnw6tWr6NatG7744gt06NABISEhRfZy1atXD/fcc8/dfgtEROQG9DnsySIiIse5zTxZX331FcLDw9GtWzf07t0bnTp1wmeffWZ53GAw4PTp08jKynJhK4mIyF3l5huRYzABYE8WERE5xi16sgAgMDAQq1atKvbxsLAwlFaN3g2q1RMRkYuYhwqqVICfp9scHomIqBxym54sIiIiJemz8wHIBEutVrm4NURE5M6YZBEREeG2yoI+HCpIRESOYZJFRESEgqIX/l5MsoiIyDFMsoiIiADoOUcWERE5CZMsIiIi3DZHFnuyiIjIQUyyiIiIwJ4sIiJyHiZZREREYOELIiJyHiZZREREuH24IOfIIiIixzDJIiIiQsE8WRwuSEREjmKSRUREhNt6sphkERGRg5hkERER4bZ5sphkERGRg5hkERER4bbCF0yyiIjIQUyyiIiIwHmyiIjIeZhkERFRpWcyCWTksvAFERE5B5MsIiKq9NJz8iGE/NnfmyXciYjIMUyyiIio0jMPFfTSquHpoXFxa4iIyN0xySIiokrPXFmQQwWJiMgZmGQREVGlx8qCRETkTEyyiIio0tOzsiARETkRkywiIqr02JNFRETOxCSLiIgqPcscWUyyiIjICZhkERFRpcfCF0RE5ExMsoiIqNJjTxYRETkTkywiIqr00rLzAQD+XpyImIiIHMcki4iIKj09C18QEZETMckiIqJKj9UFiYjImZhkERFRpWcufMF7soiIyBmYZBERUaXH4YJERORMTLKIiKhSE0KwuiARETkVkywiIqrUcgwmGIwCAHuyiIjIOZhkERFRpWbuxdKoVaii07i4NUREVBEwySIiokrNMlTQywMqlcrFrSEiooqASRYREVVq5sqCHCpIRETOwiSLiIgqtbQsFr0gIiLnYpJFRESVGnuyiIjI2dwmyUpJScHgwYPh7++PgIAAjBw5EhkZGaVut3fvXjz88MOoUqUK/P398eCDDyI7O/sutJiIiNwBy7cTEZGzuU2SNXjwYBw/fhxxcXH4/vvv8euvv+LFF18scZu9e/eiV69e6NGjB+Lj47F//36MHTsWarXbvG0iIlJYqnm4oBeTLCIicg4PVzegLE6ePIktW7Zg//79aN++PQBg3rx56N27N2bPno3atWsXud3rr7+OV199FZMmTbIsa9q06V1pMxERuYcbmbkAgGBfnYtbQkREFYVbJFl79+5FQECAJcECgOjoaKjVauzbtw9PPPFEoW2SkpKwb98+DB48GB07dsT58+cRHh6O999/H506dSr2tXJzc5Gbm2v5Xa/XAwAMBgMMBoMT35XtzK/v6nZUVIyvshhfZTG+9ktMywEABPp4FBs/xldZjK/yGGNlMb7KKk/xLWsbVEIIoXBbHPbBBx9gxYoVOH36tNXyGjVqIDY2FqNHjy60ze+//46oqCgEBgZi9uzZaNOmDb744gssXLgQx44dQ+PGjYt8renTpyM2NrbQ8lWrVsHHx8c5b4iIiMqNucc0uJCuwogmRrSpXu4PiURE5EJZWVkYNGgQ0tLS4O/vX+x6Lu3JmjRpEmbNmlXiOidPnrTruU0mEwDgpZdewogRIwAAbdu2xbZt27B06VLMnDmzyO0mT56MmJgYy+96vR6hoaHo0aNHiYG8GwwGA+Li4tC9e3dotbx3wNkYX2UxvspifO03+9QuANno8eD9aF+/WpHrML7KYnyVxxgri/FVVnmKr3mUW2lcmmSNHz8ew4cPL3GdBg0aICQkBElJSVbL8/PzkZKSgpCQkCK3q1WrFgCgefPmVsubNWuGS5cuFft6np6e8PT0LLRcq9W6/EM1K09tqYgYX2UxvspifG13IzMPAFAroEqpsWN8lcX4Ko8xVhbjq6zyEN+yvr5Lk6zg4GAEBweXul5UVBRSU1Nx4MABtGvXDgCwfft2mEwmREZGFrlNWFgYateuXWiI4ZkzZ/DII4843ngiInJ7mbn5yMozAgCC/ApfYCMiIrKHW9Qyb9asGXr16oVRo0YhPj4eu3fvxtixYzFgwABLZcGrV68iPDwc8fHxAACVSoWJEyfi008/xbfffotz587hnXfewalTpzBy5EhXvh0iIionrmfIQkfeWg2q6DQubg0REVUUblFdEAC++uorjB07Ft26dYNarcZTTz2FTz/91PK4wWDA6dOnkZWVZVk2btw45OTk4PXXX0dKSgpat26NuLg4NGzY0BVvgYiIyhlzkhXkp4NKpXJxa4iIqKJwmyQrMDAQq1atKvbxsLAwFFUocdKkSVbzZBEREZklp5vnyOJQQSIich63GC5IRESkhOQMWfQiiEkWERE5EZMsIiKqtK6nm4cLMskiIiLnYZJFRESVVnIGhwsSEZHzMckiIqJKiz1ZRESkBCZZRERUabEni4iIlMAki4iIKi1zCfdgP52LW0JERBUJkywiIqqUhBC4ni6rCwb7erm4NUREVJEwySIiokopM8+IbIMRgJyMmIiIyFmYZBERUaVkLnrho9PAR+fh4tYQEVFFwiSLiIgqJUvRC1YWJCIiJ2OSRURElZKlfDsrCxIRkZMxySIiokrpOsu3ExGRQphkERFRpZRsmYiYRS+IiMi5mGQREVGllJzB8u1ERKQMJllERFQpsSeLiIiUwiSLiIgqJfM9WSx8QUREzsYki4iIKqXrLOFOREQKYZJFRESVjhDCMlyQ1QWJiMjZmGQREVGlk5Gbj9x8EwAOFyQiIudjkkVERJWOuRfL19MD3jqNi1tDREQVDZMsIiKqdK6by7fzfiwiIlIAkywiIqp0CioLsnw7ERE5H5MsIiKqdCxzZPF+LCIiUgCTLCIiqnRYvp2IiJTEJIuIiCod9mQREZGSmGQREVGlw54sIiJSEpMsIiKqdJJvVRdkTxYRESmBSRYREVU619PZk0VERMrxcHUDqOwGL9mPy0kaLPxrD1QqlaubU+EIIaBPZ3yVwvgqi/G1zT9p2QBYwp2IiJTBJMuNnE/OxI0sFf7JynB1UyowxldZjK+yGF9bBFbRoYafl6ubQUREFRCTLDfy6YBW2L1nHzpEdoCHhh+ds+Ub8xG/L57xVQjjqyzG13ZNQnyh8+CoeSIicj4eid1Ih7BAXD8h8EDD6tBqta5uToVjMBiQdprxVQrjqyzGl4iIqPzgJTwiIiIiIiInYpJFRERERETkREyyiIiIiIiInMhtkqyUlBQMHjwY/v7+CAgIwMiRI5GRUXIVrYSEBAwZMgQhISGoUqUK7r33Xqxbt+4utZiIiIiIiCojt0myBg8ejOPHjyMuLg7ff/89fv31V7z44oslbjN06FCcPn0amzZtwtGjR/Hkk0/i2WefxaFDh+5Sq4mIiIiIqLJxiyTr5MmT2LJlC/773/8iMjISnTp1wrx587B69Wpcu3at2O327NmDV155BR06dECDBg0wZcoUBAQE4MCBA3ex9UREREREVJm4RQn3vXv3IiAgAO3bt7csi46Ohlqtxr59+/DEE08UuV3Hjh2xZs0a9OnTBwEBAfjmm2+Qk5ODrl27Fvtaubm5yM3Ntfyu1+sByPLIBoPBOW/ITubXd3U7KirGV1mMr7IYX2UxvspifJXHGCuL8VVWeYpvWdugEkIIhdvisA8++AArVqzA6dOnrZbXqFEDsbGxGD16dJHbpaamon///ti6dSs8PDzg4+ODtWvXokePHsW+1vTp0xEbG1to+apVq+Dj4+PYGyEiIiIiIreVlZWFQYMGIS0tDf7+/sWu59KerEmTJmHWrFklrnPy5Em7n/+dd95Bamoqfv75ZwQFBeG7777Ds88+i127diEiIqLIbSZPnoyYmBjL73q9HqGhoejRo0eJgbwbDAYD4uLi0L17d042qgDGV1mMr7IYX2UxvspifJXHGCuL8VVWeYqveZRbaVyaZI0fPx7Dhw8vcZ0GDRogJCQESUlJVsvz8/ORkpKCkJCQIrc7f/485s+fj2PHjqFFixYAgNatW2PXrl1YsGABFi9eXOR2np6e8PT0LLRcq9W6/EM1K09tqYgYX2UxvspifJXF+CqL8VUeY6wsxldZ5SG+ZX19lyZZwcHBCA4OLnW9qKgopKam4sCBA2jXrh0AYPv27TCZTIiMjCxym6ysLACAWm1d20Oj0cBkMjnYciIiIiIioqK5RXXBZs2aoVevXhg1ahTi4+Oxe/dujB07FgMGDEDt2rUBAFevXkV4eDji4+MBAOHh4WjUqBFeeuklxMfH4/z58/jkk08QFxeHfv36ufDdEBERERFRReYWSRYAfPXVVwgPD0e3bt3Qu3dvdOrUCZ999pnlcYPBgNOnT1t6sLRaLTZv3ozg4GD07dsXrVq1whdffIEVK1agd+/ernobRERERERUwblFCXcACAwMxKpVq4p9PCwsDHcWSmzcuDHWrVundNOIiIiIiIgs3CbJchVz4lbWSiJKMhgMyMrKgl6vd/lNfxUR46ssxldZjK+yGF9lMb7KY4yVxfgqqzzF15wTlDYLFpOsUqSnpwMAQkNDXdwSIiIiIiIqD9LT01G1atViH3eLyYhdyWQy4dq1a/Dz84NKpXJpW8xzdl2+fNnlc3ZVRIyvshhfZTG+ymJ8lcX4Ko8xVhbjq6zyFF8hBNLT01G7du1CVcxvx56sUqjVatStW9fVzbDi7+/v8h2sImN8lcX4KovxVRbjqyzGV3mMsbIYX2WVl/iW1INl5jbVBYmIiIiIiNwBkywiIiIiIiInYpLlRjw9PTFt2jR4enq6uikVEuOrLMZXWYyvshhfZTG+ymOMlcX4Kssd48vCF0RERERERE7EniwiIiIiIiInYpJFRERERETkREyyiIiIiIiInIhJFhERERERkRMxySpH3n//fXTs2BE+Pj4ICAgo0zZCCEydOhW1atWCt7c3oqOjcfbsWat1UlJSMHjwYPj7+yMgIAAjR45ERkaGAu+g/LM1FhcvXoRKpSry39q1ay3rFfX46tWr78ZbKlfs2de6du1aKHb/+te/rNa5dOkS+vTpAx8fH9SoUQMTJ05Efn6+km+lXLI1vikpKXjllVfQtGlTeHt7o169enj11VeRlpZmtV5l3X8XLFiAsLAweHl5ITIyEvHx8SWuv3btWoSHh8PLywsRERHYvHmz1eNl+T6uTGyJ7+eff47OnTujWrVqqFatGqKjowutP3z48EL7aa9evZR+G+WWLfFdvnx5odh5eXlZrcP915ot8S3qOKZSqdCnTx/LOtx/C/z666/o27cvateuDZVKhe+++67UbXbs2IF7770Xnp6eaNSoEZYvX15oHVu/0xUnqNyYOnWqmDNnjoiJiRFVq1Yt0zYffvihqFq1qvjuu+/En3/+KR577DFxzz33iOzsbMs6vXr1Eq1btxa///672LVrl2jUqJEYOHCgQu+ifLM1Fvn5+eKff/6x+hcbGyt8fX1Fenq6ZT0AYtmyZVbr3f4ZVBb27GtdunQRo0aNsopdWlqa5fH8/HzRsmVLER0dLQ4dOiQ2b94sgoKCxOTJk5V+O+WOrfE9evSoePLJJ8WmTZvEuXPnxLZt20Tjxo3FU089ZbVeZdx/V69eLXQ6nVi6dKk4fvy4GDVqlAgICBCJiYlFrr97926h0WjERx99JE6cOCGmTJkitFqtOHr0qGWdsnwfVxa2xnfQoEFiwYIF4tChQ+LkyZNi+PDhomrVquLKlSuWdYYNGyZ69epltZ+mpKTcrbdUrtga32XLlgl/f3+r2CUkJFitw/23gK3xvXHjhlVsjx07JjQajVi2bJllHe6/BTZv3izefvttsX79egFAbNiwocT1//rrL+Hj4yNiYmLEiRMnxLx584RGoxFbtmyxrGPrZ3Y3MMkqh5YtW1amJMtkMomQkBDx8ccfW5alpqYKT09P8fXXXwshhDhx4oQAIPbv329Z58cffxQqlUpcvXrV6W0vz5wVizZt2ojnn3/eallZviQqOnvj26VLF/Haa68V+/jmzZuFWq22OiFYtGiR8Pf3F7m5uU5puztw1v77zTffCJ1OJwwGg2VZZdx/O3ToIF5++WXL70ajUdSuXVvMnDmzyPWfffZZ0adPH6tlkZGR4qWXXhJClO37uDKxNb53ys/PF35+fmLFihWWZcOGDROPP/64s5vqlmyNb2nnFdx/rTm6//773/8Wfn5+IiMjw7KM+2/RynL8eeONN0SLFi2slvXv31/07NnT8rujn5kSOFzQjV24cAEJCQmIjo62LKtatSoiIyOxd+9eAMDevXsREBCA9u3bW9aJjo6GWq3Gvn377nqbXckZsThw4AAOHz6MkSNHFnrs5ZdfRlBQEDp06IClS5dCVLIp6ByJ71dffYWgoCC0bNkSkydPRlZWltXzRkREoGbNmpZlPXv2hF6vx/Hjx53/RsopZ/0tp6Wlwd/fHx4eHlbLK9P+m5eXhwMHDlh9d6rVakRHR1u+O++0d+9eq/UBuR+a1y/L93FlYU9875SVlQWDwYDAwECr5Tt27ECNGjXQtGlTjB49Gjdu3HBq292BvfHNyMhA/fr1ERoaiscff9zq+5P7bwFn7L9LlizBgAEDUKVKFavl3H/tU9r3rzM+MyV4lL4KlVcJCQkAYHXyaf7d/FhCQgJq1Khh9biHhwcCAwMt61QWzojFkiVL0KxZM3Ts2NFq+bvvvouHH34YPj4+2Lp1K8aMGYOMjAy8+uqrTmt/eWdvfAcNGoT69eujdu3aOHLkCN58802cPn0a69evtzxvUfu4+bHKwhn77/Xr1zFjxgy8+OKLVssr2/57/fp1GI3GIverU6dOFblNcfvh7d+15mXFrVNZ2BPfO7355puoXbu21UlTr1698OSTT+Kee+7B+fPn8dZbb+GRRx7B3r17odFonPoeyjN74tu0aVMsXboUrVq1QlpaGmbPno2OHTvi+PHjqFu3Lvff2zi6/8bHx+PYsWNYsmSJ1XLuv/Yr7vtXr9cjOzsbN2/edPg7RwlMshQ2adIkzJo1q8R1Tp48ifDw8LvUooqnrDF2VHZ2NlatWoV33nmn0GO3L2vbti0yMzPx8ccfV4iTVKXje/sJf0REBGrVqoVu3brh/PnzaNiwod3P6y7u1v6r1+vRp08fNG/eHNOnT7d6rCLvv+R+PvzwQ6xevRo7duywKs4wYMAAy88RERFo1aoVGjZsiB07dqBbt26uaKrbiIqKQlRUlOX3jh07olmzZvi///s/zJgxw4Utq3iWLFmCiIgIdOjQwWo599/Kh0mWwsaPH4/hw4eXuE6DBg3seu6QkBAAQGJiImrVqmVZnpiYiDZt2ljWSUpKstouPz8fKSkplu3dXVlj7Ggsvv32W2RlZWHo0KGlrhsZGYkZM2YgNzcXnp6epa5fnt2t+JpFRkYCAM6dO4eGDRsiJCSkUIWgxMREAKgQ+/DdiG96ejp69eoFPz8/bNiwAVqttsT1K9L+W5SgoCBoNBrLfmSWmJhYbCxDQkJKXL8s38eVhT3xNZs9ezY+/PBD/Pzzz2jVqlWJ6zZo0ABBQUE4d+5cpTpJdSS+ZlqtFm3btsW5c+cAcP+9nSPxzczMxOrVq/Huu++W+jqVdf+1R3Hfv/7+/vD29oZGo3H4b0IJvCdLYcHBwQgPDy/xn06ns+u577nnHoSEhGDbtm2WZXq9Hvv27bNcsYqKikJqaioOHDhgWWf79u0wmUyWk1l3V9YYOxqLJUuW4LHHHkNwcHCp6x4+fBjVqlWrECeodyu+ZocPHwYAy4E+KioKR48etUow4uLi4O/vj+bNmzvnTbqQ0vHV6/Xo0aMHdDodNm3aVKhsc1Eq0v5bFJ1Oh3bt2ll9d5pMJmzbts3qav/toqKirNYH5H5oXr8s38eVhT3xBYCPPvoIM2bMwJYtW6zuPSzOlStXcOPGDaukoDKwN763MxqNOHr0qCV23H8LOBLftWvXIjc3F88991ypr1NZ9197lPb964y/CUW4rOQGFfL333+LQ4cOWUqEHzp0SBw6dMiqVHjTpk3F+vXrLb9/+OGHIiAgQGzcuFEcOXJEPP7440WWcG/btq3Yt2+f+O2330Tjxo0rdQn3kmJx5coV0bRpU7Fv3z6r7c6ePStUKpX48ccfCz3npk2bxOeffy6OHj0qzp49KxYuXCh8fHzE1KlTFX8/5Y2t8T137px49913xR9//CEuXLggNm7cKBo0aCAefPBByzbmEu49evQQhw8fFlu2bBHBwcGVtoS7LfFNS0sTkZGRIiIiQpw7d86qdHB+fr4QovLuv6tXrxaenp5i+fLl4sSJE+LFF18UAQEBliqWQ4YMEZMmTbKsv3v3buHh4SFmz54tTp48KaZNm1ZkCffSvo8rC1vj++GHHwqdTie+/fZbq/3UfPxLT08XEyZMEHv37hUXLlwQP//8s7j33ntF48aNRU5OjkveoyvZGt/Y2Fjx008/ifPnz4sDBw6IAQMGCC8vL3H8+HHLOtx/C9gaX7NOnTqJ/v37F1rO/ddaenq65RwXgJgzZ444dOiQ+Pvvv4UQQkyaNEkMGTLEsr65hPvEiRPFyZMnxYIFC4os4V7SZ+YKTLLKkWHDhgkAhf798ssvlnVwaz4bM5PJJN555x1Rs2ZN4enpKbp16yZOnz5t9bw3btwQAwcOFL6+vsLf31+MGDHCKnGrTEqLxYULFwrFXAghJk+eLEJDQ4XRaCz0nD/++KNo06aN8PX1FVWqVBGtW7cWixcvLnLdis7W+F66dEk8+OCDIjAwUHh6eopGjRqJiRMnWs2TJYQQFy9eFI888ojw9vYWQUFBYvz48VYlyCsLW+P7yy+/FPmdAkBcuHBBCFG599958+aJevXqCZ1OJzp06CB+//13y2NdunQRw4YNs1r/m2++EU2aNBE6nU60aNFC/PDDD1aPl+X7uDKxJb7169cvcj+dNm2aEEKIrKws0aNHDxEcHCy0Wq2oX7++GDVqlEtPoFzNlviOGzfOsm7NmjVF7969xcGDB62ej/uvNVu/H06dOiUAiK1btxZ6Lu6/1oo7NpljOmzYMNGlS5dC27Rp00bodDrRoEEDq3Nhs5I+M1dQCVGB6/QSERERERHdZbwni4iIiIiIyImYZBERERERETkRkywiIiIiIiInYpJFRERERETkREyyiIiIiIiInIhJFhERERERkRMxySIiIiIiInIiJllEREREREROxCSLiIiIiIjIiZhkERERERERORGTLCIiIiIiIidikkVERFSM5ORkhISE4IMPPrAs27NnD3Q6HbZt2+bClhERUXmmEkIIVzeCiIiovNq8eTP69euHPXv2oGnTpmjTpg0ef/xxzJkzx9VNIyKicopJFhERUSlefvll/Pzzz2jfvj2OHj2K/fv3w9PT09XNIiKicopJFhERUSmys7PRsmVLXL58GQcOHEBERISrm0REROUY78kiIiIqxfnz53Ht2jWYTCZcvHjR1c0hIqJyjj1ZREREJcjLy0OHDh3Qpk0bNG3aFHPnzsXRo0dRo0YNVzeNiIjKKSZZREREJZg4cSK+/fZb/Pnnn/D19UWXLl1QtWpVfP/9965uGhERlVMcLkhERFSMHTt2YO7cuVi5ciX8/f2hVquxcuVK7Nq1C4sWLXJ184iIqJxiTxYREREREZETsSeLiIiIiIjIiZhkERERERERORGTLCIiIiIiIidikkVERERERORETLKIiIiIiIiciEkWERERERGREzHJIiIiIiIiciImWURERERERE7EJIuIiIiIiMiJmGQRERERERE5EZMsIiIiIiIiJ/p/zQx/UEU2r5wAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -818,13 +668,13 @@ "plt.legend()\n", "plt.grid(True)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -963,24 +813,13 @@ "\n", "\n", "Y_high_low = add_gaussian_noise(high_low(X))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -995,13 +834,13 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -1021,13 +860,13 @@ "data_valid = TensorDataset(X_valid, Y_valid)\n", "loader_train = DataLoader(data_train, batch_size=batch_size, shuffle=False)\n", "loader_valid = DataLoader(data_valid, batch_size=batch_size, shuffle=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "import logging\n", @@ -1041,13 +880,13 @@ "optimizer = Adam(model.parameters(), lr=lr, amsgrad=True)\n", "loss_fn = torch.nn.MSELoss()\n", "metric = MeanAbsolutePercentageError()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "logger = logging.getLogger(\"train_function\")\n", "logger.setLevel(level=logging.INFO)\n", @@ -1059,241 +898,23 @@ " num_epochs=num_epochs,\n", " logger=logger,\n", ")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:train_function:Train - Epoch: 1, Loss: 8.342393, Metrics: MARE: 1.440194\n", - "INFO:train_function:Validate - Epoch: 1, Loss: 7.607440, Metrics: MARE: 1.366828\n", - "INFO:train_function:Train - Epoch: 2, Loss: 5.895769, Metrics: MARE: 1.212957\n", - "INFO:train_function:Validate - Epoch: 2, Loss: 5.577182, Metrics: MARE: 1.177464\n", - "INFO:train_function:Train - Epoch: 3, Loss: 4.131848, Metrics: MARE: 1.016340\n", - "INFO:train_function:Validate - Epoch: 3, Loss: 4.109858, Metrics: MARE: 1.013616\n", - "INFO:train_function:Train - Epoch: 4, Loss: 2.913098, Metrics: MARE: 0.852639\n", - "INFO:train_function:Validate - Epoch: 4, Loss: 3.092991, Metrics: MARE: 0.877199\n", - "INFO:train_function:Train - Epoch: 5, Loss: 2.072767, Metrics: MARE: 0.716553\n", - "INFO:train_function:Validate - Epoch: 5, Loss: 2.389320, Metrics: MARE: 0.763794\n", - "INFO:train_function:Train - Epoch: 6, Loss: 1.476303, Metrics: MARE: 0.599679\n", - "INFO:train_function:Validate - Epoch: 6, Loss: 1.887640, Metrics: MARE: 0.666399\n", - "INFO:train_function:Train - Epoch: 7, Loss: 1.040128, Metrics: MARE: 0.494947\n", - "INFO:train_function:Validate - Epoch: 7, Loss: 1.518669, Metrics: MARE: 0.579122\n", - "INFO:train_function:Train - Epoch: 8, Loss: 0.720350, Metrics: MARE: 0.398397\n", - "INFO:train_function:Validate - Epoch: 8, Loss: 1.246001, Metrics: MARE: 0.498664\n", - "INFO:train_function:Train - Epoch: 9, Loss: 0.494334, Metrics: MARE: 0.308837\n", - "INFO:train_function:Validate - Epoch: 9, Loss: 1.050949, Metrics: MARE: 0.424031\n", - "INFO:train_function:Train - Epoch: 10, Loss: 0.346951, Metrics: MARE: 0.226811\n", - "INFO:train_function:Validate - Epoch: 10, Loss: 0.921175, Metrics: MARE: 0.355676\n", - "INFO:train_function:Train - Epoch: 11, Loss: 0.263601, Metrics: MARE: 0.153660\n", - "INFO:train_function:Validate - Epoch: 11, Loss: 0.844856, Metrics: MARE: 0.294717\n", - "INFO:train_function:Train - Epoch: 12, Loss: 0.228201, Metrics: MARE: 0.090822\n", - "INFO:train_function:Validate - Epoch: 12, Loss: 0.808968, Metrics: MARE: 0.242352\n", - "INFO:train_function:Train - Epoch: 13, Loss: 0.224114, Metrics: MARE: 0.040548\n", - "INFO:train_function:Validate - Epoch: 13, Loss: 0.800001, Metrics: MARE: 0.200456\n", - "INFO:train_function:Train - Epoch: 14, Loss: 0.236081, Metrics: MARE: 0.079844\n", - "INFO:train_function:Validate - Epoch: 14, Loss: 0.805512, Metrics: MARE: 0.233204\n", - "INFO:train_function:Train - Epoch: 15, Loss: 0.252067, Metrics: MARE: 0.107103\n", - "INFO:train_function:Validate - Epoch: 15, Loss: 0.815635, Metrics: MARE: 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"INFO:train_function:Validate - Epoch: 21, Loss: 0.806210, Metrics: MARE: 0.235242\n", - "INFO:train_function:Train - Epoch: 22, Loss: 0.229342, Metrics: MARE: 0.062978\n", - "INFO:train_function:Validate - Epoch: 22, Loss: 0.801833, Metrics: MARE: 0.219149\n", - "INFO:train_function:Train - Epoch: 23, Loss: 0.224649, Metrics: MARE: 0.044044\n", - "INFO:train_function:Validate - Epoch: 23, Loss: 0.800057, Metrics: MARE: 0.203370\n", - "INFO:train_function:Train - Epoch: 24, Loss: 0.223018, Metrics: MARE: 0.053147\n", - "INFO:train_function:Validate - Epoch: 24, Loss: 0.800600, Metrics: MARE: 0.210956\n", - "INFO:train_function:Train - Epoch: 25, Loss: 0.223581, Metrics: MARE: 0.067561\n", - "INFO:train_function:Validate - Epoch: 25, Loss: 0.802638, Metrics: MARE: 0.222968\n", - "INFO:train_function:Train - Epoch: 26, Loss: 0.225187, Metrics: MARE: 0.078524\n", - "INFO:train_function:Validate - Epoch: 26, Loss: 0.805153, Metrics: MARE: 0.232103\n", - "INFO:train_function:Train - Epoch: 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61, Loss: 0.222995, Metrics: MARE: 0.054604\n", - "INFO:train_function:Validate - Epoch: 61, Loss: 0.800741, Metrics: MARE: 0.212170\n", - "INFO:train_function:Train - Epoch: 62, Loss: 0.222992, Metrics: MARE: 0.055017\n", - "INFO:train_function:Validate - Epoch: 62, Loss: 0.800783, Metrics: MARE: 0.212514\n", - "INFO:train_function:Train - Epoch: 63, Loss: 0.222990, Metrics: MARE: 0.055417\n", - "INFO:train_function:Validate - Epoch: 63, Loss: 0.800825, Metrics: MARE: 0.212847\n", - "INFO:train_function:Train - Epoch: 64, Loss: 0.222990, Metrics: MARE: 0.055757\n", - "INFO:train_function:Validate - Epoch: 64, Loss: 0.800862, Metrics: MARE: 0.213130\n", - "INFO:train_function:Train - Epoch: 65, Loss: 0.222990, Metrics: MARE: 0.056006\n", - "INFO:train_function:Validate - Epoch: 65, Loss: 0.800890, Metrics: MARE: 0.213339\n", - "INFO:train_function:Train - Epoch: 66, Loss: 0.222990, Metrics: MARE: 0.056151\n", - "INFO:train_function:Validate - Epoch: 66, Loss: 0.800906, Metrics: MARE: 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95, Loss: 0.222990, Metrics: MARE: 0.055467\n", - "INFO:train_function:Validate - Epoch: 95, Loss: 0.800831, Metrics: MARE: 0.212889\n", - "INFO:train_function:Train - Epoch: 96, Loss: 0.222990, Metrics: MARE: 0.055479\n", - "INFO:train_function:Validate - Epoch: 96, Loss: 0.800832, Metrics: MARE: 0.212899\n", - "INFO:train_function:Train - Epoch: 97, Loss: 0.222990, Metrics: MARE: 0.055490\n", - "INFO:train_function:Validate - Epoch: 97, Loss: 0.800833, Metrics: MARE: 0.212909\n", - "INFO:train_function:Train - Epoch: 98, Loss: 0.222990, Metrics: MARE: 0.055499\n", - "INFO:train_function:Validate - Epoch: 98, Loss: 0.800834, Metrics: MARE: 0.212916\n", - "INFO:train_function:Train - Epoch: 99, Loss: 0.222990, Metrics: MARE: 0.055504\n", - "INFO:train_function:Validate - Epoch: 99, Loss: 0.800835, Metrics: MARE: 0.212920\n", - "INFO:train_function:Train - Epoch: 100, Loss: 0.222990, Metrics: MARE: 0.055505\n", - "INFO:train_function:Validate - Epoch: 100, Loss: 0.800835, Metrics: MARE: 0.212921\n" - ] - } - ], "source": [ "# Train\n", "trainer.train(model, train_data=loader_train, valid_data=loader_valid)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Plotting\n", "X = torch.linspace(-1, 1, 300, dtype=torch.float64).reshape(-1, 1)\n", @@ -1315,87 +936,35 @@ "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter containing:\n", - "tensor([-1.9678], requires_grad=True)\n" - ] - } - ], "source": [ "print(model.observable_weights)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter containing:\n", - "tensor([0.6115, 0.7766, 0.9200, 1.0011, 0.9861, 0.9143, 0.7843, 0.6087],\n", - " dtype=torch.float64, requires_grad=True)\n", - "tensor([[0.6115+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.7766+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.9200+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 1.0011+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.9861+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.9143+0.j, 0.0000+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.7843+0.j,\n", - " 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.6087+0.j]], grad_fn=)\n" - ] - } - ], "source": [ "import torch\n", "print(model.coefficients)\n", "D = torch.diag(model.coefficients)\n", "D = D.type(torch.complex64)\n", "print(D)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0: ──Rot(5.12,2.71,1.30)─╭●──Rot(2.72,4.57,1.35)─╭●──Rot(4.07,5.89,0.10)─╭●──Rot(2.49,5.49,3.95)─┤\n", - "1: ──Rot(3.05,2.99,3.77)─╰X──Rot(1.46,4.08,1.75)─╰X──Rot(3.79,4.54,1.10)─╰X──Rot(2.56,2.23,5.75)─┤\n", - "\n", - " ╭Sample\n", - " ╰Sample\n", - "tensor([[-0.1825-0.5123j, -0.5127-0.1049j, 0.0955-0.1297j, -0.2530-0.5834j],\n", - " [-0.1597-0.0348j, 0.3080-0.6627j, 0.3754-0.1744j, -0.4636+0.2302j],\n", - " [ 0.0107-0.7958j, 0.0552+0.1769j, 0.0404+0.1368j, 0.0945+0.5504j],\n", - " [-0.1716+0.1210j, -0.2968-0.2639j, -0.0368+0.8837j, -0.0871+0.0909j]],\n", - " grad_fn=)\n" - ] - } - ], "source": [ "import torch\n", "from qulearn import qlayer\n", @@ -1416,36 +985,25 @@ "\n", "U = qml.matrix(layer.circuit)()\n", "print(U)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.6136+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " [0.0000+0.j, 0.9758+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.9746+0.j, 0.0000+0.j],\n", - " [0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.6154+0.j]],\n", - " grad_fn=)\n" - ] - } - ], "source": [ "O = U @ D @ U.conj().t()\n", "print(D)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "source": [], + "outputs": [] } ], "metadata": { diff --git a/scratch/scratch4.ipynb b/scratch/scratch4.ipynb index cbe9191..628003e 100644 --- a/scratch/scratch4.ipynb +++ b/scratch/scratch4.ipynb @@ -4,88 +4,19 @@ "cell_type": "code", "execution_count": 56, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "import tntorch as tn\n", "import pennylane as qml" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 10 5 4 1\n", - "\n", - "TT norm tensor(1.0000)\n", - "Norm core 0: tensor([[0.3487]])\n", - "Norm core 4: tensor([[1.7319]])\n", - "ID core 0: tensor([[ 0.0757, -0.0292],\n", - " [-0.0292, 0.0459]])\n", - "ID core 4: tensor([[ 0.1012, -0.1466, 0.2673, 0.3762],\n", - " [-0.1466, 0.2150, -0.3552, -0.5211],\n", - " [ 0.2673, -0.3552, 1.0784, 1.2707],\n", - " [ 0.3762, -0.5211, 1.2707, 1.6047]])\n", - "ID core 2 right: tensor([[ 3.6082, 0.8655, 0.9239, 0.1447, 0.4107],\n", - " [ 0.8655, 3.9974, -0.7578, -0.3593, 0.1775],\n", - " [ 0.9239, -0.7578, 7.7339, 2.9870, 0.1378],\n", - " [ 0.1447, -0.3593, 2.9870, 6.0718, -0.4856],\n", - " [ 0.4107, 0.1775, 0.1378, -0.4856, 7.0138]])\n", - "ID core 2 left: tensor([[ 4.0549e+00, -1.5177e-01, -8.8430e-01, -2.4782e+00, 1.4548e+00,\n", - " -5.2108e-01, -3.7645e-01, -1.3652e+00, 7.5933e-01, -5.1444e-01],\n", - " [-1.5177e-01, 9.4737e-01, 2.3455e-02, -3.1784e-01, 2.7663e-01,\n", - " 5.0287e-01, 8.1556e-01, 3.2639e-01, 1.1861e+00, -1.7724e-01],\n", - " [-8.8430e-01, 2.3455e-02, 1.7176e+00, 5.3296e-01, -1.3614e+00,\n", - " -1.6024e-02, -2.4122e-01, -8.8985e-01, -1.1925e+00, 2.1777e-01],\n", - " [-2.4782e+00, -3.1784e-01, 5.3296e-01, 2.9084e+00, -1.0749e+00,\n", - " -6.6761e-04, -2.5467e-01, 9.9021e-01, -6.0403e-01, -4.2304e-02],\n", - " [ 1.4548e+00, 2.7663e-01, -1.3614e+00, -1.0749e+00, 4.1499e+00,\n", - " 6.8497e-01, 1.5759e-01, -1.0277e+00, 2.9372e+00, -1.4246e+00],\n", - " [-5.2108e-01, 5.0287e-01, -1.6024e-02, -6.6761e-04, 6.8497e-01,\n", - " 2.0788e+00, 1.6609e+00, 1.1150e+00, 1.0020e+00, -2.8539e-01],\n", - " [-3.7645e-01, 8.1556e-01, -2.4122e-01, -2.5467e-01, 1.5759e-01,\n", - " 1.6609e+00, 3.6048e+00, 1.1099e+00, 1.7220e+00, -7.4960e-01],\n", - " [-1.3652e+00, 3.2639e-01, -8.8985e-01, 9.9021e-01, -1.0277e+00,\n", - " 1.1150e+00, 1.1099e+00, 3.4033e+00, 9.7740e-02, 2.6975e-01],\n", - " [ 7.5933e-01, 1.1861e+00, -1.1925e+00, -6.0403e-01, 2.9372e+00,\n", - " 1.0020e+00, 1.7220e+00, 9.7740e-02, 3.9436e+00, -1.3406e+00],\n", - " [-5.1444e-01, -1.7724e-01, 2.1777e-01, -4.2304e-02, -1.4246e+00,\n", - " -2.8539e-01, -7.4960e-01, 2.6975e-01, -1.3406e+00, 1.6164e+00]])\n", - "------------AFTER------------\n", - "Norm core 0: tensor([[1.4142]])\n", - "Norm core 4: tensor([[1.0000]])\n", - "ID core 0: tensor([[1.0000, 0.0000],\n", - " [0.0000, 1.0000]])\n", - "ID core 4: tensor([[ 0.0536, -0.0917, -0.1132, 0.1275],\n", - " [-0.0917, 0.1603, 0.2200, -0.2305],\n", - " [-0.1132, 0.2200, 0.4391, -0.3627],\n", - " [ 0.1275, -0.2305, -0.3627, 0.3470]])\n", - "ID core 2 right: tensor([[ 1.0000e+00, -2.1497e-08, -5.6876e-08, 3.3715e-08, -3.9321e-08],\n", - " [-2.1497e-08, 1.0000e+00, 1.6917e-08, -4.4865e-08, 2.0349e-08],\n", - " [-5.6876e-08, 1.6917e-08, 1.0000e+00, -3.0453e-08, -4.7811e-08],\n", - " [ 3.3715e-08, -4.4865e-08, -3.0453e-08, 1.0000e+00, 1.2750e-08],\n", - " [-3.9321e-08, 2.0349e-08, -4.7811e-08, 1.2750e-08, 1.0000e+00]])\n", - "ID core 2 left: tensor([[ 1.0903, -0.3303, 0.1690, 0.5503],\n", - " [-0.3303, 1.3604, 0.3068, -0.2793],\n", - " [ 0.1690, 0.3068, 1.4919, 0.0782],\n", - " [ 0.5503, -0.2793, 0.0782, 1.0575]])\n", - "\n" - ] - } - ], "source": [ "tt = tn.randn([2]*5, ranks_tt=[2, 10, 5, 4])\n", "tt /= tt.norm()\n", @@ -206,22 +137,13 @@ "print(\"ID core 2 left: \", cm)\n", "\n", "print(type(tt))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1, 2, 2, 4])\n", - "\n" - ] - } - ], "source": [ "def contract(tt, L):\n", " cores = tt.cores\n", @@ -234,21 +156,13 @@ "tmp = contract(tt, L=2)\n", "print(tmp.shape)\n", "print(type(tmp))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([2, 2, 4])\n" - ] - } - ], "source": [ "core = tt.cores[1]\n", "print(core.shape)\n", @@ -258,25 +172,13 @@ " U, _, _ = torch.linalg.svd(Q, full_matrices=True)\n", " Q_ = torch.cat((Q, U[:, k:]), dim=1)\n", " return Q_" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[4 0]\n", - " [0 4]]\n", - "False\n", - "-4.0\n", - "0: ──U(M0)─┤ \n" - ] - } - ], "source": [ "dev = qml.device('default.qubit', wires=1)\n", "U = 1 / np.sqrt(2) * np.array([[1, 1], [1, -1]])\n", @@ -297,46 +199,13 @@ "print(example_circuit())\n", "drawer = qml.draw(example_circuit, show_all_wires=True, expansion_strategy=\"device\")\n", "print(drawer())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 4 4 4 2 1\n", - "\n", - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 4 4 4 4 1\n", - "\n", - "tensor(0.)\n", - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 4 4 4 1\n", - "\n", - "tensor(0.)\n" - ] - } - ], "source": [ "tt = tn.randn([2]*5, ranks_tt=[4, 4, 4, 2])\n", "\n", @@ -403,51 +272,25 @@ "print(tt_)\n", "diff = tt - tt_\n", "print(diff.norm())" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 4 4 4 1\n", - "\n", - "torch.Size([1, 2, 2, 2, 4])\n" - ] - } - ], "source": [ "print(s)\n", "print(tt_)\n", "core = contract(tt_, L=s+1)\n", "print(core.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "torch.Size([2, 16])\n" - ] - } - ], "source": [ "# left, inner and right reshapings\n", "def left_core_reshape(core, s):\n", @@ -466,32 +309,13 @@ "print(type(core))\n", "Q = reg_core_reshape(core)\n", "print(Q.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 4 4 4 2 1\n", - "\n", - "3\n", - "torch.Size([8, 8])\n", - "torch.Size([8, 8])\n", - "torch.Size([8, 8])\n" - ] - } - ], "source": [ "# tt to unitary list\n", "def tt2unitaries(tt):\n", @@ -522,24 +346,13 @@ "\n", "for U in Us:\n", " print(U.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n", - "torch.Size([8, 8])\n", - "torch.Size([8, 8])\n", - "torch.Size([8, 8])\n" - ] - } - ], "source": [ "from qulearn.mps import MPSQGates\n", "mpsgates = MPSQGates(tt)\n", @@ -548,73 +361,13 @@ "\n", "for U in Us:\n", " print(U.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.)\n", - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 4 4 4 1\n", - "\n", - "torch.Size([8, 8])\n", - "torch.Size([1, 2, 2, 2, 4])\n", - "torch.Size([8, 4])\n", - "torch.Size([8, 4])\n", - "tensor([[-0.2215, -0.2835, 0.1553, -0.1158],\n", - " [ 0.1198, -0.2948, 0.2174, 0.5350],\n", - " [ 0.0343, -0.1878, -0.2774, -0.2362],\n", - " [ 0.3005, -0.6267, 0.4129, -0.3863],\n", - " [-0.8508, -0.0562, 0.3794, -0.1160],\n", - " [-0.2567, -0.6109, -0.6357, 0.2219],\n", - " [ 0.2188, -0.1267, 0.1062, -0.2699],\n", - " [-0.0862, 0.1112, -0.3490, -0.5998]])\n", - "tensor([[-0.2215, 0.1198, 0.0343, 0.3005],\n", - " [-0.2835, -0.2948, -0.1878, -0.6267],\n", - " [ 0.1553, 0.2174, -0.2774, 0.4129],\n", - " [-0.1158, 0.5350, -0.2362, -0.3863],\n", - " [-0.8532, 0.1663, 0.3136, 0.1325],\n", - " [-0.2999, -0.4312, -0.7473, 0.2175],\n", - " [ 0.1159, 0.0668, -0.0296, -0.3636],\n", - " [-0.0407, 0.5891, -0.4162, -0.0133]])\n", - "tensor([[-0.2215, -0.2835, 0.1553, -0.1158],\n", - " [ 0.1198, -0.2948, 0.2174, 0.5350],\n", - " [ 0.0343, -0.1878, -0.2774, -0.2362],\n", - " [ 0.3005, -0.6267, 0.4129, -0.3863],\n", - " [-0.8508, -0.0562, 0.3794, -0.1160],\n", - " [-0.2567, -0.6109, -0.6357, 0.2219],\n", - " [ 0.2188, -0.1267, 0.1062, -0.2699],\n", - " [-0.0862, 0.1112, -0.3490, -0.5998]])\n", - "tensor([[ 1.0000e+00, 8.8043e-09, -4.6874e-08, -1.3498e-08, -4.5033e-09,\n", - " 1.5863e-08, 1.5490e-09, 9.5434e-09],\n", - " [ 8.8043e-09, 1.0000e+00, -7.7029e-08, 6.8826e-08, -1.7819e-08,\n", - " 3.3268e-08, 6.0539e-09, -2.7204e-08],\n", - " [-4.6874e-08, -7.7029e-08, 1.0000e+00, -1.0998e-08, -3.4523e-08,\n", - " 1.2822e-07, -1.8700e-08, 5.8764e-08],\n", - " [-1.3498e-08, 6.8826e-08, -1.0998e-08, 1.0000e+00, -2.8051e-08,\n", - " 2.0244e-08, 5.0906e-08, 1.2412e-08],\n", - " [-4.5033e-09, -1.7819e-08, -3.4523e-08, -2.8051e-08, 1.0000e+00,\n", - " -4.7569e-08, -2.2003e-09, 7.7574e-09],\n", - " [ 1.5863e-08, 3.3268e-08, 1.2822e-07, 2.0244e-08, -4.7569e-08,\n", - " 1.0000e+00, 1.7790e-10, 5.1386e-09],\n", - " [ 1.5490e-09, 6.0539e-09, -1.8700e-08, 5.0906e-08, -2.2003e-09,\n", - " 1.7790e-10, 1.0000e+00, -1.1085e-08],\n", - " [ 9.5434e-09, -2.7204e-08, 5.8764e-08, 1.2412e-08, 7.7574e-09,\n", - " 5.1386e-09, -1.1085e-08, 1.0000e+00]])\n" - ] - } - ], "source": [ "# unitary list to circuit\n", "num_qubits = 5\n", @@ -649,88 +402,13 @@ "print(Q2_.T[:, :4])\n", "\n", "print(torch.mm(U.T, U))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 4 4 4 4 1\n", - "\n", - "0: ───────────────╭U(M2)─┤ State\n", - "1: ────────╭U(M1)─├U(M2)─┤ State\n", - "2: ─╭U(M0)─├U(M1)─╰U(M2)─┤ State\n", - "3: ─├U(M0)─╰U(M1)────────┤ State\n", - "4: ─╰U(M0)───────────────┤ State\n", - "circuit: [-0.07365078 -0.02891789 0.04293878 0.00879231 -0.11568183 0.20524918\n", - " 0.08236877 -0.03957362 0.01616416 -0.00902177 0.00623437 -0.03029924\n", - " 0.23249778 -0.29886121 -0.18842358 0.10022443 0.01141569 0.03721769\n", - " 0.01251542 0.00741011 -0.1085564 0.00099249 0.10626377 0.03202021\n", - " -0.05252615 -0.07636401 0.05182039 -0.07001516 0.50385233 -0.47584493\n", - " -0.43514359 0.16150526]\n", - "psi.T: tensor([[-0.0532, -0.1086, 0.0783, 0.0169, 0.4960, -0.5010, -0.4284, 0.2094,\n", - " -0.3013, 0.2482, 0.2344, -0.0198, 0.0449, -0.2040, -0.0554, -0.0119,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])\n", - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 4 4 4 1\n", - "\n", - "torch.Size([1, 2, 2, 2, 4])\n", - "=====================================\n", - "Q3 reshaped: tensor([[ 0.7226, -0.4620, -0.0541, -0.1402, -0.2029, 0.4181, 0.1520, -0.0525]])\n", - "U3: tensor([[ 0.7226, -0.4620, -0.0541, -0.1402, -0.2029, 0.4181, 0.1520, -0.0525]])\n", - "=====================================\n", - "=====================================\n", - "Q2 reshaped: tensor([[-0.1869, -0.3106, -0.6207, -0.4007],\n", - " [ 0.0685, -0.0093, 0.1640, 0.4053],\n", - " [ 0.5936, 0.5591, -0.3297, 0.2003],\n", - " [-0.3667, 0.6466, 0.2480, -0.5136],\n", - " [-0.5070, -0.0742, 0.0708, 0.4960],\n", - " [ 0.4402, -0.3870, 0.2676, -0.3315],\n", - " [-0.1188, 0.0841, -0.5755, 0.1221],\n", - " [-0.0921, 0.1023, -0.0986, -0.0220]])\n", - "torch.Size([4, 2, 4, 1])\n", - "U2: tensor([[-0.1869, -0.3106, -0.6207, -0.4007],\n", - " [ 0.0685, -0.0093, 0.1640, 0.4053],\n", - " [ 0.5936, 0.5591, -0.3297, 0.2003],\n", - " [-0.3667, 0.6466, 0.2480, -0.5136],\n", - " [-0.5070, -0.0742, 0.0708, 0.4960],\n", - " [ 0.4402, -0.3870, 0.2676, -0.3315],\n", - " [-0.1188, 0.0841, -0.5755, 0.1221],\n", - " [-0.0921, 0.1023, -0.0986, -0.0220]])\n", - "=====================================\n", - "Q2 shape torch.Size([4, 2, 4])\n", - "torch.Size([2, 2, 2, 4])\n", - "torch.Size([4, 2, 4])\n", - "torch.Size([4, 2, 1])\n", - "torch.Size([2, 2, 2, 2, 2, 1])\n", - "psi = tensor([[-0.0532, -0.1086, 0.0783, 0.0169, 0.4960, -0.5010, -0.4284, 0.2094,\n", - " -0.3013, 0.2482, 0.2344, -0.0198, 0.0449, -0.2040, -0.0554, -0.0119,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])\n", - "vec = tensor([-0.0532, -0.1086, 0.0783, 0.0169, 0.4960, -0.5010, -0.4284, 0.2094,\n", - " -0.3013, 0.2482, 0.2344, -0.0198, 0.0449, -0.2040, -0.0554, -0.0119,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])\n" - ] - } - ], "source": [ "num_qubits = 5\n", "tt = tn.randn([2]*num_qubits, ranks_tt=4)\n", @@ -822,43 +500,13 @@ "\n", "print(\"psi = \", psi.T)\n", "print(\"vec = \", vec)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 4 4 4 4 1\n", - "\n", - "(32,)\n", - "torch.Size([32])\n", - "[-0.07365078 -0.02891789 0.04293878 0.00879231 -0.11568183 0.20524918\n", - " 0.08236877 -0.03957362 0.01616416 -0.00902177 0.00623437 -0.03029924\n", - " 0.23249778 -0.29886121 -0.18842358 0.10022443 0.01141569 0.03721769\n", - " 0.01251542 0.00741011 -0.1085564 0.00099249 0.10626377 0.03202021\n", - " -0.05252615 -0.07636401 0.05182039 -0.07001516 0.50385233 -0.47584493\n", - " -0.43514359 0.16150526]\n", - "=======\n", - "tensor([-0.0737, -0.0289, 0.0429, 0.0088, -0.1157, 0.2052, 0.0824, -0.0396,\n", - " 0.0162, -0.0090, 0.0062, -0.0303, 0.2325, -0.2989, -0.1884, 0.1002,\n", - " 0.0114, 0.0372, 0.0125, 0.0074, -0.1086, 0.0010, 0.1063, 0.0320,\n", - " -0.0525, -0.0764, 0.0518, -0.0700, 0.5039, -0.4758, -0.4351, 0.1615])\n", - "================DIFF===================\n", - "tensor(2.4191e-07, dtype=torch.float64)\n" - ] - } - ], "source": [ "psi = ttunitaries2circuit(Us, num_qubits, s)\n", "ttfull = tt.torch().reshape(2**num_qubits)\n", @@ -871,13 +519,13 @@ "print(\"================DIFF===================\")\n", "diff = torch.tensor(psi.real) - ttfull\n", "print(torch.linalg.norm(diff))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import torch\n", @@ -903,33 +551,13 @@ " #norms = torch.linalg.norm(values, dim=1, keepdim=True)\n", " #values /= norms\n", " return values.squeeze(0)#, norms" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x = tensor([-2.0000, -1.0000, -0.9900, 0.0000, -0.3000, 0.9000, 1.0000, 1.3000],\n", - " dtype=torch.float64)\n", - "Left Points: tensor([-1.2857, -1.0000, -1.0000, -0.1429, -0.4286, 0.7143, 1.0000, 1.0000],\n", - " dtype=torch.float64)\n", - "Right Points: tensor([-1.0000, -0.7143, -0.7143, 0.1429, -0.1429, 1.0000, 1.2857, 1.2857],\n", - " dtype=torch.float64)\n", - "Position: tensor([-1., 0., 0., 3., 2., 6., 7., -2.], dtype=torch.float64)\n", - "First: tensor([ 3.5000, 1.0000, 0.9650, 0.5000, 0.5500, 0.3500, 1.0000, -0.0500],\n", - " dtype=torch.float64)\n", - "Second: tensor([-2.5000, 0.0000, 0.0350, 0.5000, 0.4500, 0.6500, 0.0000, 1.0500],\n", - " dtype=torch.float64)\n", - "Should sum to one: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n", - " dtype=torch.float64)\n" - ] - } - ], "source": [ "def find_grid_points(x, n, a=-1.0, b=1.0):\n", " num_segments = 2**n - 1\n", @@ -987,33 +615,13 @@ "print(\"First: \", first)\n", "print(\"Second: \", second)\n", "print(\"Should sum to one: \", first+second)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3D TT tensor:\n", - "\n", - " 2 2 2\n", - " | | |\n", - " (0) (1) (2)\n", - " / \\ / \\ / \\\n", - "1 1 1 1\n", - "\n", - "pos: 1\n", - "tt vectorized:\n", - "tensor([0.9500, 0.0500, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])\n", - "tensor([0.0000, 0.9500, 0.0500, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", - " dtype=torch.float64)\n" - ] - } - ], "source": [ "# TT for even case\n", "def tt_lin1dfembasis_evenidx(first, second, idx, n):\n", @@ -1050,67 +658,23 @@ "print(tt.torch().reshape(2**n))\n", "Phi = linear_FEM_basis(x, n)\n", "print(Phi)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello there\n" - ] - } - ], "source": [ "print(\"hello \"\\\n", " \"there\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "k: 13\n", - "binary: 01101\n", - "zero bit: 3\n", - "TT EVEN\n", - "3D TT tensor:\n", - "\n", - " 2 2 2\n", - " | | |\n", - " (0) (1) (2)\n", - " / \\ / \\ / \\\n", - "1 1 1 1\n", - "\n", - "TT ODD\n", - "3D TT tensor:\n", - "\n", - " 2 2 2\n", - " | | |\n", - " (0) (1) (2)\n", - " / \\ / \\ / \\\n", - "1 1 2 1\n", - "\n", - "pos: 5\n", - "tt_even vectorized:\n", - "tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.2250, 0.7750, 0.0000, 0.0000])\n", - "tt_odd vectorized:\n", - "tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2250, 0.7750, 0.0000])\n", - "exact Phi:\n", - "tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2250, 0.7750, 0.0000],\n", - " dtype=torch.float64)\n" - ] - } - ], "source": [ "# TT for odd case\n", "import math\n", @@ -1193,26 +757,13 @@ "Phi = linear_FEM_basis(x, n)\n", "print(\"exact Phi:\")\n", "print(Phi)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phi\n", - "tensor([0.0000, 0.0000, 0.9000, 0.1000], dtype=torch.float64)\n", - "psi\n", - "[0. 0. 0.89999998 0.1 ]\n", - "tensor([ 0.0000e+00, 0.0000e+00, -2.3842e-08, 1.4901e-09],\n", - " dtype=torch.float64)\n" - ] - } - ], "source": [ "num_qubits = 2\n", "dev = qml.device('default.qubit', wires=num_qubits)\n", @@ -1258,24 +809,13 @@ "print(\"psi\")\n", "print(psi.real)\n", "print(torch.tensor(psi.real)-Phi)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", @@ -1309,13 +849,13 @@ "for m in range(M):\n", " basis_functions[m, :] = torch.tensor(linear1dFEMcircuit(x_plot[m], num_qubits, sqrt=True))\n", "plot_basis_combinations(x_plot, basis_functions, num_qubits)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -1398,33 +938,13 @@ "\n", " self.norm = torch.sqrt(first*first + second*second)\n", " return self.norm" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 2 2 2 2 1\n", - "\n", - "4\n", - "torch.Size([4, 4])\n", - "torch.Size([4, 4])\n", - "torch.Size([4, 4])\n", - "torch.Size([4, 4])\n" - ] - } - ], "source": [ "from qulearn.hat_basis import HatBasis\n", "from qulearn.mps import HatBasisMPS, MPSQGates\n", @@ -1445,13 +965,13 @@ "print(len(Us))\n", "for U in Us:\n", " print(U.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -1520,13 +1040,13 @@ " self.weights[mps_layer_idx, block_idx, 1, block_layer, 2],\n", " qnext)\n", " qml.CNOT(wires=(qprev, qnext))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "from qulearn import qlayer\n", @@ -1541,33 +1061,13 @@ "\n", " def circuit(self, _):\n", " qml.QubitUnitary(self.U, wires=self.wires, unitary_check=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Input unitary must be of shape (8, 8) or (batch_size, 8, 8) to act on 3 wires.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 42\u001b[0m\n\u001b[1;32m 40\u001b[0m drawer \u001b[38;5;241m=\u001b[39m qml\u001b[38;5;241m.\u001b[39mdraw(model\u001b[38;5;241m.\u001b[39mqnode, show_all_wires\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, expansion_strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdevice\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 41\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.1\u001b[39m])\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdrawer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 44\u001b[0m drawer \u001b[38;5;241m=\u001b[39m qml\u001b[38;5;241m.\u001b[39mdraw(model2\u001b[38;5;241m.\u001b[39mqnode, show_all_wires\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, expansion_strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdevice\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 45\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.1\u001b[39m])\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/pennylane/drawer/draw.py:190\u001b[0m, in \u001b[0;36mdraw..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 189\u001b[0m qnode\u001b[38;5;241m.\u001b[39mexpansion_strategy \u001b[38;5;241m=\u001b[39m expansion_strategy \u001b[38;5;129;01mor\u001b[39;00m original_expansion_strategy\n\u001b[0;32m--> 190\u001b[0m tapes \u001b[38;5;241m=\u001b[39m \u001b[43mqnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 192\u001b[0m qnode\u001b[38;5;241m.\u001b[39mexpansion_strategy \u001b[38;5;241m=\u001b[39m original_expansion_strategy\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/pennylane/qnode.py:711\u001b[0m, in \u001b[0;36mQNode.construct\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconstruct\u001b[39m(\u001b[38;5;28mself\u001b[39m, args, kwargs):\n\u001b[1;32m 709\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Call the quantum function with a tape context, ensuring the operations get queued.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tape \u001b[38;5;241m=\u001b[39m \u001b[43mmake_qscript\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tape\u001b[38;5;241m.\u001b[39m_queue_category \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ops\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 713\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qfunc_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtape\u001b[38;5;241m.\u001b[39m_qfunc_output\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/pennylane/tape/qscript.py:1346\u001b[0m, in \u001b[0;36mmake_qscript..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1345\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m AnnotatedQueue() \u001b[38;5;28;01mas\u001b[39;00m q:\n\u001b[0;32m-> 1346\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1348\u001b[0m qscript \u001b[38;5;241m=\u001b[39m QuantumScript\u001b[38;5;241m.\u001b[39mfrom_queue(q)\n\u001b[1;32m 1349\u001b[0m qscript\u001b[38;5;241m.\u001b[39m_qfunc_output \u001b[38;5;241m=\u001b[39m result\n", - "File \u001b[0;32m~/Research/QC/QuLearn/qulearn/qlayer.py:1051\u001b[0m, in \u001b[0;36mHamiltonianLayer.expectation\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;124;03mCompute the expectation of the Hamiltonian.\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;124;03m:rtype: Expectation\u001b[39;00m\n\u001b[1;32m 1049\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m circuit \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcircuits:\n\u001b[0;32m-> 1051\u001b[0m \u001b[43mcircuit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobservable \u001b[38;5;241m=\u001b[39m qml\u001b[38;5;241m.\u001b[39mHamiltonian(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobservable_weights, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobservables)\n\u001b[1;32m 1053\u001b[0m expec \u001b[38;5;241m=\u001b[39m qml\u001b[38;5;241m.\u001b[39mexpval(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobservable)\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "File \u001b[0;32m~/Research/QC/QuLearn/qulearn/qlayer.py:69\u001b[0m, in \u001b[0;36mCircuitLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 68\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Forward pass. See :meth:`circuit`\"\"\"\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcircuit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[79], line 13\u001b[0m, in \u001b[0;36membedU.circuit\u001b[0;34m(self, _)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcircuit\u001b[39m(\u001b[38;5;28mself\u001b[39m, _):\n\u001b[0;32m---> 13\u001b[0m \u001b[43mqml\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQubitUnitary\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mU\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwires\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwires\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitary_check\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/pennylane/ops/qubit/matrix_ops.py:87\u001b[0m, in \u001b[0;36mQubitUnitary.__init__\u001b[0;34m(self, U, wires, do_queue, id, unitary_check)\u001b[0m\n\u001b[1;32m 84\u001b[0m dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(wires)\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(U_shape) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m} \u001b[38;5;129;01mor\u001b[39;00m U_shape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m:] \u001b[38;5;241m!=\u001b[39m (dim, dim):\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 88\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput unitary must be of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m(dim,\u001b[38;5;250m \u001b[39mdim)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or (batch_size, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto act on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(wires)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m wires.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 90\u001b[0m )\n\u001b[1;32m 92\u001b[0m \u001b[38;5;66;03m# Check for unitarity; due to variable precision across the different ML frameworks,\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# here we issue a warning to check the operation, instead of raising an error outright.\u001b[39;00m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unitary_check \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\n\u001b[1;32m 95\u001b[0m qml\u001b[38;5;241m.\u001b[39mmath\u001b[38;5;241m.\u001b[39mis_abstract(U)\n\u001b[1;32m 96\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m qml\u001b[38;5;241m.\u001b[39mmath\u001b[38;5;241m.\u001b[39mallclose(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 100\u001b[0m )\n\u001b[1;32m 101\u001b[0m ):\n", - "\u001b[0;31mValueError\u001b[0m: Input unitary must be of shape (8, 8) or (batch_size, 8, 8) to act on 3 wires." - ] - } - ], "source": [ "from qulearn import qlayer\n", "import pennylane as qml\n", @@ -1618,24 +1118,13 @@ "\n", "nump = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(\"Number of parameters: \", nump)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -1658,13 +1147,13 @@ "plt.legend()\n", "plt.grid(True)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import torch\n", "\n", @@ -1803,24 +1292,13 @@ "\n", "\n", "Y_high_low = add_gaussian_noise(high_low(X))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -1833,23 +1311,13 @@ "\n", "# Show the plot\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-02-05 15:57:49.621766: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-02-05 15:57:50.640817: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" - ] - } - ], "source": [ "import torch\n", "import logging\n", @@ -1869,27 +1337,13 @@ "data_valid = TensorDataset(X_valid, Y_valid)\n", "loader_train = DataLoader(data_train, batch_size=batch_size, shuffle=False)\n", "loader_valid = DataLoader(data_valid, batch_size=batch_size, shuffle=False)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "optimizer got an empty parameter list", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Trainer\u001b[39;00m\n\u001b[1;32m 9\u001b[0m lr \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.1\u001b[39m\n\u001b[0;32m---> 10\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m \u001b[43mAdam\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mMSELoss()\n\u001b[1;32m 12\u001b[0m metric \u001b[38;5;241m=\u001b[39m MeanAbsolutePercentageError()\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/torch/optim/adam.py:33\u001b[0m, in \u001b[0;36mAdam.__init__\u001b[0;34m(self, params, lr, betas, eps, weight_decay, amsgrad, foreach, maximize, capturable, differentiable, fused)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weight_decay value: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(weight_decay))\n\u001b[1;32m 29\u001b[0m defaults \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(lr\u001b[38;5;241m=\u001b[39mlr, betas\u001b[38;5;241m=\u001b[39mbetas, eps\u001b[38;5;241m=\u001b[39meps,\n\u001b[1;32m 30\u001b[0m weight_decay\u001b[38;5;241m=\u001b[39mweight_decay, amsgrad\u001b[38;5;241m=\u001b[39mamsgrad,\n\u001b[1;32m 31\u001b[0m maximize\u001b[38;5;241m=\u001b[39mmaximize, foreach\u001b[38;5;241m=\u001b[39mforeach, capturable\u001b[38;5;241m=\u001b[39mcapturable,\n\u001b[1;32m 32\u001b[0m differentiable\u001b[38;5;241m=\u001b[39mdifferentiable, fused\u001b[38;5;241m=\u001b[39mfused)\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fused:\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m differentiable:\n", - "File \u001b[0;32m/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/torch/optim/optimizer.py:187\u001b[0m, in \u001b[0;36mOptimizer.__init__\u001b[0;34m(self, params, defaults)\u001b[0m\n\u001b[1;32m 185\u001b[0m param_groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(params)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(param_groups) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimizer got an empty parameter list\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(param_groups[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m 189\u001b[0m param_groups \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m: param_groups}]\n", - "\u001b[0;31mValueError\u001b[0m: optimizer got an empty parameter list" - ] - } - ], "source": [ "import torch\n", "import logging\n", @@ -1903,13 +1357,13 @@ "optimizer = Adam(model.parameters(), lr=lr, amsgrad=True)\n", "loss_fn = torch.nn.MSELoss()\n", "metric = MeanAbsolutePercentageError()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "logger = logging.getLogger(\"train_function\")\n", "logger.setLevel(level=logging.INFO)\n", @@ -1921,241 +1375,23 @@ " num_epochs=num_epochs,\n", " logger=logger,\n", ")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, 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MARE: 854.410020\n", - "INFO:train_function:Validate - Epoch: 99, Loss: 0.000507, Metrics: MARE: 8548.310547\n", - "INFO:train_function:Train - Epoch: 100, Loss: 0.000488, Metrics: MARE: 963.434374\n", - "INFO:train_function:Validate - Epoch: 100, Loss: 0.000668, Metrics: MARE: 10005.293945\n" - ] - } - ], "source": [ "# Train\n", "trainer.train(model, train_data=loader_train, valid_data=loader_valid)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Plotting\n", "X = torch.linspace(-1, 1, 300, dtype=torch.float64).reshape(-1, 1)\n", @@ -2177,25 +1413,13 @@ "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Matrix: [[ 1.+0.j 0.-1.89961775j 0.+0.22658997j 0.-0.40975701j]\n", - " [ 0.+1.89961775j 0.+0.j 0.+0.41457242j 0.-0.29266821j]\n", - " [ 0.-0.22658997j 0.-0.41457242j 0.+0.j 0.+0.02567953j]\n", - " [ 0.+0.40975701j 0.+0.29266821j 0.-0.02567953j -1.+0.j ]]\n", - "Eigenvalues: [-1.71251902 -0.88035468 0.05090306 2.54197065]\n" - ] - } - ], "source": [ "import numpy as np\n", "\n", @@ -2220,39 +1444,13 @@ "# Print the eigenvalues\n", "print(\"Matrix:\", H)\n", "print(\"Eigenvalues:\", eigenvalues)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/scipy/optimize/_differentiable_functions.py:107: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " self.x = np.atleast_1d(x0).astype(float)\n", - "/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/scipy/optimize/_differentiable_functions.py:243: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " self.x = np.atleast_1d(x).astype(float)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization failed.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/software/anaconda3/envs/QuLearn/lib/python3.11/site-packages/scipy/optimize/_differentiable_functions.py:243: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " self.x = np.atleast_1d(x).astype(float)\n" - ] - } - ], "source": [ "import numpy as np\n", "import scipy.optimize\n", @@ -2324,21 +1522,13 @@ " print(D_optimal)\n", "else:\n", " print(\"Optimization failed.\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{1.61803398874989: 1, -0.618033988749895: 1, 0: 1, -1.00000000000000: 1}\n" - ] - } - ], "source": [ "import sympy as sp\n", "\n", @@ -2362,29 +1552,13 @@ "\n", "# Output the eigenvalues\n", "print(eigenvalues)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues: [ 1. -1. 1. -1.]\n", - "Eigenvectors: [[-0. +0.j -0. +0.j 1. +0.j\n", - " 0. +0.j ]\n", - " [ 0.70710678+0.j 0.70710678+0.j 0. +0.j\n", - " 0. +0.j ]\n", - " [-0. -0.70710678j -0. +0.70710678j 0. +0.j\n", - " 0. +0.j ]\n", - " [-0. +0.j -0. +0.j 0. +0.j\n", - " 1. +0.j ]]\n" - ] - } - ], "source": [ "import numpy as np\n", "\n", @@ -2408,39 +1582,13 @@ "# Output the eigenvalues and eigenvectors\n", "print(\"Eigenvalues:\", np.real(eigenvalues))\n", "print(\"Eigenvectors:\", eigenvectors)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimized values for the imaginary parts of c1 to c6: [-2.14680367e-08 -2.02883951e-08 8.84280981e-10 9.99999993e-01\n", - " -1.96976737e-08 -2.11726760e-08]\n", - "Cost value: 1.8154602478292675e-15\n", - "Unitary: [[ 7.65580316e-01+0.00000000e+00j 6.43340330e-01+0.00000000e+00j\n", - " 8.03059170e-09+0.00000000e+00j 6.69081704e-09+0.00000000e+00j]\n", - " [-3.12458146e-01+3.30625615e-01j 3.71827787e-01-3.93447209e-01j\n", - " -3.58264593e-01-4.06936451e-01j -3.28064884e-01-3.13723825e-01j]\n", - " [ 3.30625607e-01+3.12458154e-01j -3.93447216e-01-3.71827780e-01j\n", - " 4.06936456e-01-3.58264585e-01j 3.13723818e-01-3.28064892e-01j]\n", - " [-6.56406532e-09+8.42710728e-11j 7.81129205e-09-7.87540829e-10j\n", - " 6.38767739e-01+6.38293834e-02j -7.66675112e-01-1.05289402e-02j]]\n", - "Check: [[ 1.00000000e+00+0.00000000e+00j 1.38777878e-16+3.32759494e-18j\n", - " 1.38777878e-16-8.50014503e-17j 2.77555756e-17-5.72458747e-17j]\n", - " [ 1.38777878e-16-3.32759494e-18j 1.00000000e+00+0.00000000e+00j\n", - " -1.66533454e-16+4.51028104e-17j -2.77555756e-17+3.98986399e-17j]\n", - " [ 1.38777878e-16+8.50014503e-17j -1.66533454e-16-4.51028104e-17j\n", - " 1.00000000e+00+0.00000000e+00j -2.49800181e-16+9.71445147e-17j]\n", - " [ 2.77555756e-17+5.72458747e-17j -2.77555756e-17-3.98986399e-17j\n", - " -2.49800181e-16-9.71445147e-17j 1.00000000e+00+0.00000000e+00j]]\n" - ] - } - ], "source": [ "import numpy as np\n", "from scipy.optimize import minimize\n", @@ -2499,28 +1647,13 @@ "check = np.dot(U.conj().T, U)\n", "print(\"Unitary:\", U)\n", "print(\"Check:\", check)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimized values for the imaginary parts of c1 to c28: [ 3.97240827e-07 2.21497248e-08 -2.18192295e-07 1.34687411e-08\n", - " -1.20533485e-07 6.00647559e-03 1.32884085e-03 5.15256883e-07\n", - " -3.02343867e-08 -1.37517176e-06 2.86333692e-07 -2.77546457e-04\n", - " 6.00659574e-03 6.70015841e-01 4.84930268e-06 7.42347073e-01\n", - " 3.05031464e-07 -1.26732259e-07 7.42346847e-01 -4.77853558e-06\n", - " -1.35762768e-06 1.60213403e-08 6.70015627e-01 -2.92447307e-08\n", - " -2.90785382e-07 4.64011657e-07 6.02180525e-09 -5.82713674e-07]\n", - "Cost value: 1.4152096399592191e-09\n" - ] - } - ], "source": [ "import numpy as np\n", "from scipy.optimize import minimize\n", @@ -2574,58 +1707,25 @@ "optimized_c = result.x\n", "print(\"Optimized values for the imaginary parts of c1 to c28:\", optimized_c)\n", "print(\"Cost value:\", result.fun)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.+0.00000000e+00j 0.+3.97240827e-07j 0.+2.21497248e-08j\n", - " 0.-2.18192295e-07j 0.+1.34687411e-08j 0.-1.20533485e-07j\n", - " 0.+6.00647559e-03j 0.+1.32884085e-03j]\n", - " [ 0.-3.97240827e-07j 1.+0.00000000e+00j 0.+5.15256883e-07j\n", - " 0.-3.02343867e-08j 0.-1.37517176e-06j 0.+2.86333692e-07j\n", - " 0.-2.77546457e-04j 0.+6.00659574e-03j]\n", - " [ 0.-2.21497248e-08j 0.-5.15256883e-07j 0.+0.00000000e+00j\n", - " 0.+6.70015841e-01j 0.+4.84930268e-06j 0.+7.42347073e-01j\n", - " 0.+3.05031464e-07j 0.-1.26732259e-07j]\n", - " [ 0.+2.18192295e-07j 0.+3.02343867e-08j 0.-6.70015841e-01j\n", - " 0.+0.00000000e+00j 0.+7.42346847e-01j 0.-4.77853558e-06j\n", - " 0.-1.35762768e-06j 0.+1.60213403e-08j]\n", - " [ 0.-1.34687411e-08j 0.+1.37517176e-06j 0.-4.84930268e-06j\n", - " 0.-7.42346847e-01j 0.+0.00000000e+00j 0.+6.70015627e-01j\n", - " 0.-2.92447307e-08j 0.-2.90785382e-07j]\n", - " [ 0.+1.20533485e-07j 0.-2.86333692e-07j 0.-7.42347073e-01j\n", - " 0.+4.77853558e-06j 0.-6.70015627e-01j 0.+0.00000000e+00j\n", - " 0.+4.64011657e-07j 0.+6.02180525e-09j]\n", - " [ 0.-6.00647559e-03j 0.+2.77546457e-04j 0.-3.05031464e-07j\n", - " 0.+1.35762768e-06j 0.+2.92447307e-08j 0.-4.64011657e-07j\n", - " -1.+0.00000000e+00j 0.-5.82713674e-07j]\n", - " [ 0.-1.32884085e-03j 0.-6.00659574e-03j 0.+1.26732259e-07j\n", - " 0.-1.60213403e-08j 0.+2.90785382e-07j 0.-6.02180525e-09j\n", - " 0.+5.82713674e-07j -1.+0.00000000e+00j]]\n", - "[-1.00002176 -1.00001531 -1.0000002 -0.99999983 0.99999983 1.0000002\n", - " 1.00001533 1.00002173]\n" - ] - } - ], "source": [ "H = construct_matrix(optimized_c)\n", "print(H)\n", "eigs, U = np.linalg.eigh(H)\n", "print(eigs)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import pennylane as qml\n", @@ -2641,13 +1741,13 @@ "# Example usage\n", "U1 = generate_random_unitary(num_qubits)\n", "U2 = generate_random_unitary(num_qubits)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "\n", @@ -2723,23 +1823,13 @@ " qml.Hadamard(num_qubits-1)\n", " I0 = qml.Projector(basis_state=[0]*(num_qubits-1), wires=list(range(0, num_qubits-1))) \n", " return qml.expval(I0 @ qml.PauliZ(num_qubits-1))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "True\n", - "tensor(-0.0010, dtype=torch.float64, grad_fn=)\n" - ] - } - ], "source": [ "import pennylane as qml\n", "import torch\n", @@ -2765,120 +1855,13 @@ "print(initlayer_weights.requires_grad)\n", "print(cost.requires_grad)\n", "print(cost)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Step 0 cost = -0.0344344\n", - "Step 1 cost = -0.1233621\n", - "Step 2 cost = -0.3059672\n", - "Step 3 cost = -0.5988825\n", - "Step 4 cost = -0.8126607\n", - "Step 5 cost = -1.1314695\n", - "Step 6 cost = -1.7148277\n", - "Step 7 cost = -2.2287950\n", - "Step 8 cost = -2.7610040\n", - "Step 9 cost = -3.3439719\n", - "Step 10 cost = -3.5235141\n", - "Step 11 cost = -3.5696547\n", - "Step 12 cost = -3.8151699\n", - "Step 13 cost = -3.8460813\n", - "Step 14 cost = -4.1434242\n", - "Step 15 cost = -4.0593428\n", - "Step 16 cost = -4.2144792\n", - "Step 17 cost = -4.4050545\n", - "Step 18 cost = -4.6152069\n", - "Step 19 cost = -4.8191031\n", - "Step 20 cost = -5.1110369\n", - "Step 21 cost = -5.6183837\n", - "Step 22 cost = -6.0586526\n", - "Step 23 cost = -6.4535227\n", - "Step 24 cost = -6.9916346\n", - "Step 25 cost = -7.2834748\n", - "Step 26 cost = -7.5450682\n", - "Step 27 cost = -7.2421023\n", - "Step 28 cost = -9.2416178\n", - "Step 29 cost = -9.3476557\n", - "Step 30 cost = -8.5366928\n", - "Step 31 cost = -10.2625045\n", - "Step 32 cost = -9.1719756\n", - "Step 33 cost = -9.8920038\n", - "Step 34 cost = -10.1591320\n", - "Step 35 cost = -9.8664078\n", - "Step 36 cost = -10.6620375\n", - "Step 37 cost = -10.1642827\n", - "Step 38 cost = -10.8350436\n", - "Step 39 cost = -10.5358955\n", - "Step 40 cost = -10.7757793\n", - "Step 41 cost = -10.7334007\n", - "Step 42 cost = -10.8827956\n", - "Step 43 cost = -10.8056472\n", - "Step 44 cost = -11.1136666\n", - "Step 45 cost = -10.9828462\n", - "Step 46 cost = -11.1887404\n", - "Step 47 cost = -11.2844386\n", - "Step 48 cost = -11.2215815\n", - "Step 49 cost = -11.3767321\n", - "Step 50 cost = -11.3723748\n", - "Step 51 cost = -11.3859645\n", - "Step 52 cost = -11.4388293\n", - "Step 53 cost = -11.5227217\n", - "Step 54 cost = -11.5015141\n", - "Step 55 cost = -11.4776323\n", - "Step 56 cost = -11.5253672\n", - "Step 57 cost = -11.5249588\n", - "Step 58 cost = -11.4869767\n", - "Step 59 cost = -11.5258163\n", - "Step 60 cost = -11.5623334\n", - "Step 61 cost = -11.5244025\n", - "Step 62 cost = -11.5447513\n", - "Step 63 cost = -11.5748977\n", - "Step 64 cost = -11.5872955\n", - "Step 65 cost = -11.5541539\n", - "Step 66 cost = -11.5907788\n", - "Step 67 cost = -11.5967359\n", - "Step 68 cost = -11.6153038\n", - "Step 69 cost = -11.5950834\n", - "Step 70 cost = -11.6107133\n", - "Step 71 cost = -11.6220808\n", - "Step 72 cost = -11.6366193\n", - "Step 73 cost = -11.6189537\n", - "Step 74 cost = -11.6230058\n", - "Step 75 cost = -11.6237361\n", - "Step 76 cost = -11.6349230\n", - "Step 77 cost = -11.6373382\n", - "Step 78 cost = -11.6318095\n", - "Step 79 cost = -11.6311878\n", - "Step 80 cost = -11.6316378\n", - "Step 81 cost = -11.6410019\n", - "Step 82 cost = -11.6455987\n", - "Step 83 cost = -11.6495897\n", - "Step 84 cost = -11.6488941\n", - "Step 85 cost = -11.6495407\n", - "Step 86 cost = -11.6465290\n", - "Step 87 cost = -11.6489104\n", - "Step 88 cost = -11.6495874\n", - "Step 89 cost = -11.6517066\n", - "Step 90 cost = -11.6545979\n", - "Step 91 cost = -11.6551771\n", - "Step 92 cost = -11.6564259\n", - "Step 93 cost = -11.6575783\n", - "Step 94 cost = -11.6586536\n", - "Step 95 cost = -11.6598739\n", - "Step 96 cost = -11.6595623\n", - "Step 97 cost = -11.6600508\n", - "Step 98 cost = -11.6586958\n", - "Step 99 cost = -11.6554679\n" - ] - } - ], "source": [ "import torch\n", "steps = 100\n", @@ -2894,27 +1877,13 @@ " opt.step(closure)\n", " cost = cost_poisson(initlayer_weights, weights)\n", " print(\"Step {:3d} cost = {:9.7f}\".format(it, cost))" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([ 0.0541+0.j, 0.1001+0.j, 0.1412+0.j, 0.1726+0.j, 0.2013+0.j, 0.2228+0.j,\n", - " 0.2374+0.j, 0.2457+0.j, 0.2452+0.j, 0.2386+0.j, 0.2246+0.j, 0.2016+0.j,\n", - " 0.1729+0.j, 0.1391+0.j, 0.0945+0.j, 0.0452+0.j, -0.0152+0.j, -0.0692+0.j,\n", - " -0.1141+0.j, -0.1551+0.j, -0.1871+0.j, -0.2054+0.j, -0.2183+0.j, -0.2243+0.j,\n", - " -0.2320+0.j, -0.2261+0.j, -0.2129+0.j, -0.1922+0.j, -0.1673+0.j, -0.1344+0.j,\n", - " -0.0960+0.j, -0.0490+0.j], dtype=torch.complex128,\n", - " grad_fn=)\n" - ] - } - ], "source": [ "import pennylane as qml\n", "\n", @@ -2925,13 +1894,13 @@ "\n", "psi_vec = -extract_psi(initlayer_weights, weights, num_qubits, num_layers)\n", "print(psi_vec)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, - "outputs": [], "source": [ "import pennylane as qml\n", "import numpy as np\n", @@ -2967,491 +1936,13 @@ " #print(\"Basis\", Ub)\n", " \n", " return result" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x tensor([-1.], dtype=torch.float64)\n", - "state tensor([1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " dtype=torch.complex128)\n", - "0: ──I─┤ State\n", - "1: ──I─┤ State\n", - "2: ──I─┤ State\n", - "3: ──I─┤ State\n", - "4: ──I─┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([-0.7778], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 2\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([-0.7778], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 2\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "x tensor([-0.7778], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.4472+0.j, 0.8944+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([-0.7778], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 2\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "0: ──U(M0)─┤ State\n", - "1: ──U(M0)─┤ State\n", - "2: ──U(M0)─┤ State\n", - "3: ──U(M1)─┤ State\n", - "4: ──U(M2)─┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([-0.5556], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 6\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([-0.5556], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 6\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "x tensor([-0.5556], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.8944+0.j,\n", - " 0.4472+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([-0.5556], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 6\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "0: ──U(M0)─┤ State\n", - "1: ──U(M0)─┤ State\n", - "2: ──U(M3)─┤ State\n", - "3: ──U(M1)─┤ State\n", - "4: ──U(M2)─┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([-0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([0.], dtype=torch.float64)\n", - "position = 10\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([-0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([0.], dtype=torch.float64)\n", - "position = 10\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "x tensor([-0.3333], dtype=torch.float64)\n", - "state tensor([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([-0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([0.], dtype=torch.float64)\n", - "position = 10\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "0: ──U(M1)─┤ State\n", - "1: ──U(M2)─┤ State\n", - "2: ──U(M1)─┤ State\n", - "3: ──U(M0)─┤ State\n", - "4: ──U(M3)─┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([-0.1111], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 13\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([-0.1111], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 13\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "x tensor([-0.1111], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.4472+0.j,\n", - " 0.8944+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([-0.1111], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 13\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "0: ──────────────────────╭U(M2)─┤ State\n", - "1: ───────────────╭U(M1)─╰U(M2)─┤ State\n", - "2: ────────╭U(M1)─╰U(M1)────────┤ State\n", - "3: ─╭U(M0)─╰U(M1)───────────────┤ State\n", - "4: ─╰U(M0)──────────────────────┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([0.1111], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 17\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([0.1111], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 17\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "x tensor([0.1111], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.8944+0.j, 0.4472+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([0.1111], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 17\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "0: ──────────────────────╭U(M3)─┤ State\n", - "1: ───────────────╭U(M2)─╰U(M3)─┤ State\n", - "2: ────────╭U(M1)─╰U(M2)────────┤ State\n", - "3: ─╭U(M0)─╰U(M1)───────────────┤ State\n", - "4: ─╰U(M0)──────────────────────┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([1.8319e-15], dtype=torch.float64)\n", - "position = 21\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([1.8319e-15], dtype=torch.float64)\n", - "position = 21\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "x tensor([0.3333], dtype=torch.float64)\n", - "state tensor([0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 1.0000e+00+0.j, 1.8319e-15+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j, 0.0000e+00+0.j,\n", - " 0.0000e+00+0.j, 0.0000e+00+0.j], dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([0.3333], dtype=torch.float64)\n", - "first = tensor([1.], dtype=torch.float64)\n", - "second = tensor([1.8319e-15], dtype=torch.float64)\n", - "position = 21\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 2 1\n", - "\n", - "+++++++++++++\n", - "0: ──────────────────────╭U(M2)─┤ State\n", - "1: ───────────────╭U(M1)─╰U(M2)─┤ State\n", - "2: ────────╭U(M1)─╰U(M1)────────┤ State\n", - "3: ─╭U(M0)─╰U(M1)───────────────┤ State\n", - "4: ─╰U(M0)──────────────────────┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([0.5556], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 24\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([0.5556], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 24\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "x tensor([0.5556], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.4472+0.j, 0.8944+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([0.5556], dtype=torch.float64)\n", - "first = tensor([0.4472], dtype=torch.float64)\n", - "second = tensor([0.8944], dtype=torch.float64)\n", - "position = 24\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "0: ──U(M3)─┤ State\n", - "1: ──U(M1)─┤ State\n", - "2: ──U(M0)─┤ State\n", - "3: ──U(M0)─┤ State\n", - "4: ──U(M2)─┤ State\n", - "==============\n", - "+++++++++++++\n", - "x = tensor([0.7778], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 28\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "+++++++++++++\n", - "x = tensor([0.7778], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 28\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "x tensor([0.7778], dtype=torch.float64)\n", - "state tensor([0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j, 0.0000+0.j,\n", - " 0.8944+0.j, 0.4472+0.j, 0.0000+0.j, 0.0000+0.j],\n", - " dtype=torch.complex128)\n", - "+++++++++++++\n", - "x = tensor([0.7778], dtype=torch.float64)\n", - "first = tensor([0.8944], dtype=torch.float64)\n", - "second = tensor([0.4472], dtype=torch.float64)\n", - "position = 28\n", - "mps tree = 5D TT tensor:\n", - "\n", - " 2 2 2 2 2\n", - " | | | | |\n", - " (0) (1) (2) (3) (4)\n", - " / \\ / \\ / \\ / \\ / \\\n", - "1 1 1 1 1 1\n", - "\n", - "+++++++++++++\n", - "0: ──U(M3)─┤ State\n", - "1: ──U(M0)─┤ State\n", - "2: ──U(M0)─┤ State\n", - "3: ──U(M1)─┤ State\n", - "4: ──U(M2)─┤ State\n", - "==============\n", - "x tensor([1.], dtype=torch.float64)\n", - "state tensor([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,\n", - " 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],\n", - " dtype=torch.complex128)\n", - "0: ──X─┤ State\n", - "1: ──X─┤ State\n", - "2: ──X─┤ State\n", - "3: ──X─┤ State\n", - "4: ──X─┤ State\n", - "==============\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import numpy as np\n", "import math\n", @@ -3501,38 +1992,13 @@ "plt.legend()\n", "plt.grid(True)\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.925481690479002+0j)\n", - "-0.9254816802045847\n", - "=====================\n", - "(0.03674334778005489+0j)\n", - "0.03674311255339645\n", - "=====================\n" - ] - }, - { - "ename": "ValueError", - "evalue": "shapes (8,8) and (32,) not aligned: 8 (dim 1) != 32 (dim 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[173], line 25\u001b[0m\n\u001b[1;32m 23\u001b[0m I \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39meye(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(n\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 24\u001b[0m IX \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mkron(I, X)\n\u001b[0;32m---> 25\u001b[0m exact \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(x, \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 26\u001b[0m quantum \u001b[38;5;241m=\u001b[39m even\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(exact)\n", - "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: shapes (8,8) and (32,) not aligned: 8 (dim 1) != 32 (dim 0)" - ] - } - ], "source": [ "x = psi_vec.detach().numpy()\n", "innerp_exact = np.dot(x, b)\n", @@ -3581,7 +2047,8 @@ "quantum = odd_IX\n", "print(exact)\n", "print(quantum.item())" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/scratch/scratch5.ipynb b/scratch/scratch5.ipynb new file mode 100644 index 0000000..5f7e466 --- /dev/null +++ b/scratch/scratch5.ipynb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "6e4cb30e217e595f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.081128Z", + "start_time": "2024-07-18T23:00:30.077756Z" + } + }, + "source": [ + "import torch\n", + "import pennylane as qml\n", + "\n", + "from qulearn.hat_basis import HatBasis\n", + "from qulearn.qlayer import (HatBasisQFE,\n", + " CircuitLayer,\n", + " MeasurementLayer,\n", + " MeasurementType)\n", + "from qulearn.mps import HatBasisMPS" + ], + "outputs": [], + "execution_count": 40 + }, + { + "cell_type": "code", + "id": "8d60b58b23b4e5f3", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.111188Z", + "start_time": "2024-07-18T23:00:30.106502Z" + } + }, + "source": [ + "import torch\n", + "import tntorch as tn\n", + "\n", + "def zkron(t1, t2):\n", + " c1 = t1.cores\n", + " c2 = t2.cores\n", + " c3 = [torch.kron(a, b) for a, b in zip(c1, c2)]\n", + " \n", + " t3 = tn.Tensor(c3)\n", + " return t3\n", + "\n", + "def zkron2(tleft, tright):\n", + " # assuming same length of left and right\n", + " coresleft = tleft.cores\n", + " coresright = tright.cores\n", + " coresout = []\n", + " \n", + " for i in range(len(coresleft)):\n", + " coreleft = coresleft[i]\n", + " coreright = coresright[i]\n", + " rankleft1 = coreleft.shape[0]\n", + " rankleft2 = coreleft.shape[-1]\n", + " rankright1 = coreright.shape[0]\n", + " rankright2 = coreright.shape[-1]\n", + " \n", + " site_dim = coreleft.shape[1]\n", + " core = torch.empty((rankleft1*rankright1, site_dim, rankleft2*rankright1))\n", + " for k in range(site_dim):\n", + " core[:, k, :] = torch.kron(coreleft[:, k, :], torch.eye(rankright1))\n", + " coresout.append(core)\n", + " \n", + " site_dim = coreright.shape[1]\n", + " core = torch.empty((rankleft2*rankright1, site_dim, rankleft2*rankright2))\n", + " for k in range(site_dim):\n", + " core[:, k, :] = torch.kron(torch.eye(rankleft2), coreright[:, k, :])\n", + " coresout.append(core)\n", + " \n", + " tout = tn.Tensor(coresout)\n", + " return tout\n", + "\n", + "\n", + "def kron(t1, t2):\n", + " c1 = t1.cores\n", + " c2 = t2.cores\n", + " c3 = c1 + c2\n", + " t3 = tn.Tensor(c3)\n", + " \n", + " return t3" + ], + "outputs": [], + "execution_count": 41 + }, + { + "cell_type": "code", + "id": "9e4e98216ac5dfb8", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.135050Z", + "start_time": "2024-07-18T23:00:30.129479Z" + } + }, + "source": [ + "import tntorch as tn\n", + "import numpy as np\n", + "\n", + "t1 = tn.randn([2]*3)\n", + "t2 = tn.ones([2]*3)\n", + "\n", + "T1 = t1.numpy().reshape((2**3))\n", + "T2 = t2.numpy().reshape((2**3))\n", + "\n", + "t3 = kron(t1, t2)\n", + "T3 = t3.numpy().reshape((2**6))\n", + "\n", + "T3_ = np.kron(T1, T2)\n", + "delta = abs(T3_ - T3)\n", + "delta = np.linalg.norm(delta)\n", + "print(\"delta: \", delta)\n", + "\n", + "t4 = zkron(t1, t2)\n", + "t5 = zkron2(t1, t2)\n", + "T4 = t4.numpy().reshape((2**6))\n", + "T5 = t5.numpy().reshape((2**6))\n", + "delta = abs(T4 - T5)\n", + "delta = np.linalg.norm(delta)\n", + "\n", + "print(T3)\n", + "print(\"=========\")\n", + "print(T4)\n", + "print(\"=========\")\n", + "print(T5)\n", + "print(\"=========\")\n", + "print(\"delta: \", delta)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "delta: 0.0\n", + "[-1.1171911 -1.1171911 -1.1171911 -1.1171911 -1.1171911 -1.1171911\n", + " -1.1171911 -1.1171911 -0.5655495 -0.5655495 -0.5655495 -0.5655495\n", + " -0.5655495 -0.5655495 -0.5655495 -0.5655495 -0.0676304 -0.0676304\n", + " -0.0676304 -0.0676304 -0.0676304 -0.0676304 -0.0676304 -0.0676304\n", + " -0.05885036 -0.05885036 -0.05885036 -0.05885036 -0.05885036 -0.05885036\n", + " -0.05885036 -0.05885036 0.32007828 0.32007828 0.32007828 0.32007828\n", + " 0.32007828 0.32007828 0.32007828 0.32007828 0.10783434 0.10783434\n", + " 0.10783434 0.10783434 0.10783434 0.10783434 0.10783434 0.10783434\n", + " 0.39209718 0.39209718 0.39209718 0.39209718 0.39209718 0.39209718\n", + " 0.39209718 0.39209718 0.15642546 0.15642546 0.15642546 0.15642546\n", + " 0.15642546 0.15642546 0.15642546 0.15642546]\n", + "=========\n", + "[-1.1171911 -1.1171911 -0.5655495 -0.5655495 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -0.0676304 -0.0676304 -0.05885036 -0.05885036\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -1.1171911 -1.1171911 -0.5655495 -0.5655495\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -0.0676304 -0.0676304\n", + " -0.05885036 -0.05885036 0.32007828 0.32007828 0.10783434 0.10783434\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.39209718 0.39209718\n", + " 0.15642546 0.15642546 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.32007828 0.32007828\n", + " 0.10783434 0.10783434 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.39209718 0.39209718 0.15642546 0.15642546]\n", + "=========\n", + "[-1.1171911 -1.1171911 -0.5655495 -0.5655495 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -0.0676304 -0.0676304 -0.05885036 -0.05885036\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -1.1171911 -1.1171911 -0.5655495 -0.5655495\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -0.0676304 -0.0676304\n", + " -0.05885036 -0.05885036 0.32007828 0.32007828 0.10783434 0.10783434\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.39209718 0.39209718\n", + " 0.15642546 0.15642546 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.32007828 0.32007828\n", + " 0.10783434 0.10783434 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.39209718 0.39209718 0.15642546 0.15642546]\n", + "=========\n", + "delta: 0.0\n" + ] + } + ], + "execution_count": 42 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.137647Z", + "start_time": "2024-07-18T23:00:30.135828Z" + } + }, + "cell_type": "code", + "source": [ + "print(t1)\n", + "for c in t1.cores:\n", + " print(c.shape[0])" + ], + "id": "81aca14a0bd6e607", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3D TT tensor:\n", + "\n", + " 2 2 2\n", + " | | |\n", + " (0) (1) (2)\n", + " / \\ / \\ / \\\n", + "1 2 2 1\n", + "\n", + "1\n", + "2\n", + "2\n" + ] + } + ], + "execution_count": 43 + }, + { + "cell_type": "code", + "id": "ed6556db86940912", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.145590Z", + "start_time": "2024-07-18T23:00:30.142532Z" + } + }, + "source": [ + "print(t1.numpy().reshape((2**3)))\n", + "print(t2.numpy().reshape((2**3)))\n", + "print(T3_)\n", + "print(T4)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1.1171911 -0.5655495 -0.0676304 -0.05885036 0.32007828 0.10783434\n", + " 0.39209718 0.15642546]\n", + "[1. 1. 1. 1. 1. 1. 1. 1.]\n", + "[-1.1171911 -1.1171911 -1.1171911 -1.1171911 -1.1171911 -1.1171911\n", + " -1.1171911 -1.1171911 -0.5655495 -0.5655495 -0.5655495 -0.5655495\n", + " -0.5655495 -0.5655495 -0.5655495 -0.5655495 -0.0676304 -0.0676304\n", + " -0.0676304 -0.0676304 -0.0676304 -0.0676304 -0.0676304 -0.0676304\n", + " -0.05885036 -0.05885036 -0.05885036 -0.05885036 -0.05885036 -0.05885036\n", + " -0.05885036 -0.05885036 0.32007828 0.32007828 0.32007828 0.32007828\n", + " 0.32007828 0.32007828 0.32007828 0.32007828 0.10783434 0.10783434\n", + " 0.10783434 0.10783434 0.10783434 0.10783434 0.10783434 0.10783434\n", + " 0.39209718 0.39209718 0.39209718 0.39209718 0.39209718 0.39209718\n", + " 0.39209718 0.39209718 0.15642546 0.15642546 0.15642546 0.15642546\n", + " 0.15642546 0.15642546 0.15642546 0.15642546]\n", + "[-1.1171911 -1.1171911 -0.5655495 -0.5655495 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -0.0676304 -0.0676304 -0.05885036 -0.05885036\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -1.1171911 -1.1171911\n", + " -0.5655495 -0.5655495 -1.1171911 -1.1171911 -0.5655495 -0.5655495\n", + " -0.0676304 -0.0676304 -0.05885036 -0.05885036 -0.0676304 -0.0676304\n", + " -0.05885036 -0.05885036 0.32007828 0.32007828 0.10783434 0.10783434\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.39209718 0.39209718\n", + " 0.15642546 0.15642546 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.32007828 0.32007828 0.10783434 0.10783434 0.32007828 0.32007828\n", + " 0.10783434 0.10783434 0.39209718 0.39209718 0.15642546 0.15642546\n", + " 0.39209718 0.39209718 0.15642546 0.15642546]\n" + ] + } + ], + "execution_count": 44 + }, + { + "cell_type": "code", + "id": "f5d359f0ae8df759", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.149319Z", + "start_time": "2024-07-18T23:00:30.146392Z" + } + }, + "source": [ + "import tntorch\n", + "try:\n", + " from typing import TypeAlias\n", + "except ImportError:\n", + " from typing_extensions import TypeAlias\n", + "MPS: TypeAlias = tntorch.tensor.Tensor\n", + "Tensor: TypeAlias = torch.Tensor\n", + "\n", + "class LinearBasis2DMPS:\n", + " def __init__(self, basis: HatBasis, zorder: bool = False) -> None:\n", + " self.basis = basis\n", + "\n", + " num_qubits = 2*math.log2(basis.num_nodes)\n", + " if not num_qubits.is_integer():\n", + " raise ValueError(\n", + " f\"Number of nodes ({basis.num_nodes}) \" \"must be a power of 2.\"\n", + " )\n", + "\n", + " self.num_sites = int(num_qubits)\n", + " self.basis1Dmps = HatBasisMPS(basis)\n", + " self.zorder = zorder\n", + " \n", + " def __call__(self, x: Tensor) -> MPS:\n", + " \"\"\"\n", + " Constructs the MPS of the hat basis evaluated at a given point x.\n", + "\n", + " :param x: The input at which to evaluate the hat basis.\n", + " :type x: Tensor\n", + " :returns: The MPS at point x.\n", + " :rtype: MPS\n", + " \"\"\"\n", + "\n", + " return self.eval(x)\n", + "\n", + " def eval(self, x: Tensor) -> MPS:\n", + " \"\"\"\n", + " Constructs the MPS of the hat basis evaluated at a given point x.\n", + "\n", + " :param x: The input at which to evaluate the hat basis.\n", + " :type x: Tensor\n", + " :returns: The MPS at point x.\n", + " :rtype: MPS\n", + " \"\"\"\n", + " \n", + " mpsx = self.basis1Dmps(x[0])\n", + " mpsy = self.basis1Dmps(x[1])\n", + " \n", + " if self.zorder:\n", + " return zkron2(mpsx, mpsy)\n", + " \n", + " return kron(mpsx, mpsy)" + ], + "outputs": [], + "execution_count": 45 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.157567Z", + "start_time": "2024-07-18T23:00:30.155644Z" + } + }, + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "class SignModelWrapper(nn.Module):\n", + " def __init__(self, old_model):\n", + " super(SignModelWrapper, self).__init__()\n", + " self.old_model = old_model\n", + "\n", + " def forward(self, x):\n", + " real_value_output = self.old_model(x)\n", + " binary_output = torch.sign(real_value_output)\n", + " return binary_output" + ], + "id": "9afa0015a6baf6fc", + "outputs": [], + "execution_count": 46 + }, + { + "cell_type": "code", + "id": "557b395bbcf03f54", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.175626Z", + "start_time": "2024-07-18T23:00:30.164604Z" + } + }, + "source": [ + "from qulearn.qlayer import AltRotCXLayer, HamiltonianLayer, ParallelIQPEncoding, HatBasis, Linear2DBasisQFE\n", + "base = 3.0\n", + "omega = 1.0\n", + "num_qubits = 3\n", + "num_nodes = 2**num_qubits\n", + "a = -1.0\n", + "b = 1.0\n", + "hat_basis = HatBasis(a=a, b=b, num_nodes=num_nodes)\n", + "var = AltRotCXLayer(wires=2*num_qubits, n_layers=3)\n", + "\n", + "embed = Linear2DBasisQFE(wires=2*num_qubits, basis=hat_basis, sqrt=True, normalize=False, zorder=True)\n", + "#embed = ParallelIQPEncoding(wires=2*num_qubits, num_features=2, n_repeat=1, base=base, omega=omega)\n", + "obs = qml.PauliZ(5)\n", + "model = MeasurementLayer(embed, var, observables=obs, measurement_type=MeasurementType.Expectation)\n", + "\n", + "obs = [qml.PauliZ(j) for j in range(2*num_qubits)]\n", + "obs += [qml.PauliX(j) for j in range(2*num_qubits)]\n", + "obs += [qml.PauliY(j) for j in range(2*num_qubits)]\n", + "model = HamiltonianLayer(embed, var, observables=obs)\n", + "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", + "x = torch.tensor([0.99, -0.99])\n", + "x = torch.tensor([0., 0.])\n", + "print(drawer(x))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: ──────────────────────────────────────────────────╭U(M3)────────────────Rot(3.89,3.99,5.77)─╭●\n", + "1: ─────────────────────────────╭U(M2)───────────────├U(M3)────────────────Rot(3.44,2.04,5.01)─╰X\n", + "2: ────────╭U(M1)───────────────├U(M2)───────────────╰U(M3)────────────────Rot(0.99,5.41,2.80)─╭●\n", + "3: ─╭U(M0)─├U(M1)───────────────╰U(M2)────────────────Rot(0.01,2.31,1.80)──────────────────────╰X\n", + "4: ─├U(M0)─╰U(M1)────────────────Rot(3.15,4.69,5.42)─╭●────────────────────Rot(3.79,4.93,1.17)───\n", + "5: ─╰U(M0)──Rot(2.73,2.27,0.44)──────────────────────╰X────────────────────Rot(0.58,3.38,4.80)───\n", + "\n", + "───Rot(6.17,2.88,4.65)─────────────────────────╭●──Rot(5.85,2.01,2.37)─────────────────────────╭●\n", + "───Rot(3.37,4.82,2.84)─╭●──Rot(5.07,5.28,4.99)─╰X──Rot(2.22,0.76,3.71)─╭●──Rot(0.75,5.06,6.23)─╰X\n", + "───Rot(1.62,1.33,2.46)─╰X──Rot(1.90,3.07,1.15)─╭●──Rot(6.25,0.08,5.93)─╰X──Rot(1.25,5.91,3.32)─╭●\n", + "───Rot(1.94,1.20,2.43)─╭●──Rot(5.26,3.68,5.31)─╰X──Rot(0.64,5.17,5.18)─╭●──Rot(0.27,3.61,3.72)─╰X\n", + "───────────────────────╰X──Rot(5.91,0.06,0.64)─╭●──Rot(0.76,4.20,5.93)─╰X──Rot(2.73,4.25,2.20)─╭●\n", + "───────────────────────────────────────────────╰X──Rot(6.04,4.09,5.67)─────────────────────────╰X\n", + "\n", + "───Rot(4.53,3.56,5.54)─────────────────────────┤ ╭<𝓗>\n", + "───Rot(3.06,5.28,5.47)─╭●──Rot(3.67,0.73,1.24)─┤ ├<𝓗>\n", + "───Rot(0.06,4.13,1.87)─╰X──Rot(4.98,2.80,1.27)─┤ ├<𝓗>\n", + "───Rot(0.55,5.01,1.00)─╭●──Rot(4.24,1.02,0.11)─┤ ├<𝓗>\n", + "───Rot(0.09,0.83,3.41)─╰X──Rot(5.63,6.28,3.42)─┤ ├<𝓗>\n", + "───Rot(5.76,2.78,0.96)─────────────────────────┤ ╰<𝓗>\n" + ] + } + ], + "execution_count": 47 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.869817Z", + "start_time": "2024-07-18T23:00:30.176537Z" + } + }, + "cell_type": "code", + "source": [ + "from qulearn.qkernel import QKernel\n", + "import torch\n", + "\n", + "embed_pptn = Linear2DBasisQFE(wires=2*num_qubits, basis=hat_basis, sqrt=True, normalize=False, zorder=True)\n", + "embed_angle = ParallelIQPEncoding(wires=2*num_qubits, num_features=2, n_repeat=1, base=base, omega=omega)\n", + "\n", + "ntrain = 10\n", + "num_features = 2\n", + "X_train = 1.98*torch.rand((ntrain, num_features)) - 0.99\n", + "kernel_model = QKernel(embed_pptn, X_train)\n", + "kernel_classifier = SignModelWrapper(kernel_model)\n", + "\n", + "scores = kernel_model(X_train)\n", + "labels = kernel_classifier(X_train)\n", + "\n", + "print(scores)\n", + "print(\"=========\")\n", + "print(labels)" + ], + "id": "1fe4363cc12446da", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([-1.7533, 1.5878, -0.2078, -1.6954, -1.9461, 0.8931, 0.8289, 0.3857,\n", + " 1.6959, 2.0537], grad_fn=)\n", + "=========\n", + "tensor([-1., 1., -1., -1., -1., 1., 1., 1., 1., 1.],\n", + " grad_fn=)\n" + ] + } + ], + "execution_count": 48 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.943576Z", + "start_time": "2024-07-18T23:00:30.870454Z" + } + }, + "cell_type": "code", + "source": [ + "from sklearn.datasets import make_moons, make_classification\n", + "import matplotlib.pyplot as plt\n", + "X, y = make_moons(n_samples=100, noise=0.2, random_state=42)\n", + "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42)\n", + "\n", + "X_min = X.min()\n", + "X_max = X.max()\n", + "X= 1.98 * (X - X_min) / (X_max - X_min) - 0.99\n", + "\n", + "# Plot the dataset\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='blue', label='Class 0')\n", + "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='red', label='Class 1')\n", + "plt.title(\"Moons Dataset\")\n", + "plt.xlabel(\"Feature 1\")\n", + "plt.ylabel(\"Feature 2\")\n", + "plt.legend()\n", + "plt.show()" + ], + "id": "91befded4d0058f7", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 49 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.947918Z", + "start_time": "2024-07-18T23:00:30.944687Z" + } + }, + "cell_type": "code", + "source": [ + "import torch\n", + "from torch.utils.data import TensorDataset, DataLoader, random_split\n", + "from sklearn.datasets import make_moons\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np\n", + "\n", + "# Split into training and validation sets\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Convert to PyTorch tensors\n", + "X_train_tensor = torch.tensor(X_train, dtype=torch.float64)\n", + "X_val_tensor = torch.tensor(X_val, dtype=torch.float64)\n", + "y_train_tensor = torch.tensor(y_train, dtype=torch.float64)\n", + "y_val_tensor = torch.tensor(y_val, dtype=torch.float64)\n", + "\n", + "# Convert y values from {0, 1} to {-1, 1}\n", + "y_train_tensor = 2 * y_train_tensor - 1\n", + "y_val_tensor = 2 * y_val_tensor - 1\n", + "\n", + "# Create TensorDatasets\n", + "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n", + "val_dataset = TensorDataset(X_val_tensor, y_val_tensor)\n", + "\n", + "# Create DataLoaders\n", + "train_dataloader = DataLoader(train_dataset, batch_size=len(train_dataset), shuffle=True)\n", + "val_dataloader = DataLoader(val_dataset, batch_size=len(val_dataset), shuffle=False)\n" + ], + "id": "6d393e274c87452b", + "outputs": [], + "execution_count": 50 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:00:30.949892Z", + "start_time": "2024-07-18T23:00:30.948588Z" + } + }, + "cell_type": "code", + "source": [ + "from qulearn.trainer import RidgeRegression\n", + "trainer = RidgeRegression(lambda_reg=1.0)" + ], + "id": "96dac59a56787378", + "outputs": [], + "execution_count": 51 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:01.656612Z", + "start_time": "2024-07-18T23:00:30.950440Z" + } + }, + "cell_type": "code", + "source": "trainer.train(kernel_model, train_dataloader, val_dataloader)", + "id": "3388e29bfda8c98b", + "outputs": [], + "execution_count": 52 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:18.891305Z", + "start_time": "2024-07-18T23:01:01.657284Z" + } + }, + "cell_type": "code", + "source": [ + "kernel_classifier = SignModelWrapper(kernel_model)\n", + "\n", + "X_tensor = torch.tensor(X, dtype=torch.float64)\n", + "y = kernel_classifier(X_tensor)\n", + "\n", + "# Plot the dataset\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(X[y == -1][:, 0], X[y == -1][:, 1], color='blue', label='Class 0')\n", + "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='red', label='Class 1')\n", + "plt.title(\"Moons Dataset\")\n", + "plt.xlabel(\"Feature 1\")\n", + "plt.ylabel(\"Feature 2\")\n", + "plt.legend()\n", + "plt.show()\n" + ], + "id": "dffd107e47feed5a", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 53 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:18.893610Z", + "start_time": "2024-07-18T23:01:18.891877Z" + } + }, + "cell_type": "code", + "source": "model = SignModelWrapper(model)", + "id": "5ad9df503d305219", + "outputs": [], + "execution_count": 54 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:36.477187Z", + "start_time": "2024-07-18T23:01:18.894153Z" + } + }, + "cell_type": "code", + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Define the number of points in each dimension\n", + "num_pnts = 30\n", + "\n", + "# Generate a grid of x and y values\n", + "x = torch.linspace(-0.99, 0.99, num_pnts)\n", + "y = torch.linspace(-0.99, 0.99, num_pnts)\n", + "X, Y = torch.meshgrid(x, y)\n", + "Z = torch.empty(num_pnts, num_pnts)\n", + "\n", + "# Evaluate the model at each point in the grid\n", + "for i in range(num_pnts):\n", + " for j in range(num_pnts):\n", + " xy = torch.tensor([[X[i, j], Y[i, j]]])\n", + " Z[i, j] = model(xy).item()\n", + "\n", + "# Convert tensors to numpy arrays for plotting\n", + "X = X.numpy()\n", + "Y = Y.numpy()\n", + "Z = Z.numpy()\n", + "\n", + "# Create a 3D surface plot\n", + "fig = plt.figure(figsize=(10, 6))\n", + "plt.imshow(Z, extent=[-1, 1, -1, 1], origin='lower', cmap='viridis', aspect='auto')\n", + "\n", + "# Add labels and title\n", + "plt.xlabel('$x$')\n", + "plt.ylabel('$y$')\n", + "plt.title(\"$\\langle Z_0\\\\rangle$\")\n", + "\n", + "# Save the figure\n", + "plt.colorbar()\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ], + "id": "4b6a40bff231c2ce", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 55 + }, + { + "cell_type": "code", + "id": "66ba7a519125ef4e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:54.087452Z", + "start_time": "2024-07-18T23:01:36.478936Z" + } + }, + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import numpy as np\n", + "\n", + "# Define the number of points in each dimension\n", + "num_pnts = 30\n", + "\n", + "# Generate a grid of x and y values\n", + "x = torch.linspace(-0.99, 0.99, num_pnts)\n", + "y = torch.linspace(-0.99, 0.99, num_pnts)\n", + "X, Y = torch.meshgrid(x, y)\n", + "Z = torch.empty(num_pnts, num_pnts)\n", + "\n", + "# Evaluate the model at each point in the grid\n", + "for i in range(num_pnts):\n", + " for j in range(num_pnts):\n", + " xy = torch.tensor([[X[i, j], Y[i, j]]])\n", + " Z[i, j] = model(xy).item()\n", + "\n", + "# Convert tensors to numpy arrays for plotting\n", + "X = X.numpy()\n", + "Y = Y.numpy()\n", + "Z = Z.numpy()\n", + "\n", + "# Create a 3D surface plot\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "surf = ax.plot_surface(X, Y, Z, cmap='viridis')\n", + "\n", + "# Add labels and title\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$')\n", + "ax.set_zlabel('$z$')\n", + "ax.set_title(\"$\\langle Z_0\\\\rangle$\")\n", + "\n", + "ax.view_init(elev=30, azim=45)\n", + "# Add a color bar which maps values to colors\n", + "fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)\n", + "\n", + "# Save the figure\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 56 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:54.094406Z", + "start_time": "2024-07-18T23:01:54.087960Z" + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "num_qubits = 2\n", + "num_nodes = 2**num_qubits\n", + "a = -1.0\n", + "b = 1.0\n", + "hat_basis = HatBasis(a=a, b=b, num_nodes=num_nodes)\n", + "\n", + "embed = Linear2DBasisQFE(wires=2*num_qubits, basis=hat_basis, sqrt=True, normalize=False, zorder=False)\n", + "\n", + "dev = qml.device(\"default.qubit\", wires=2*num_qubits)\n", + "@qml.qnode(dev)\n", + "def circuit(x):\n", + " embed.circuit(x)\n", + " return qml.state()\n", + "\n", + "x = torch.tensor([-0.99, -0.])\n", + "out = np.real(circuit(x))\n", + "print(out)\n", + "print(\"norm: \", np.linalg.norm(out))" + ], + "id": "93646da4c54dfbff", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0.70178351 0.08660251\n", + " 0. 0. 0.70178351 0.08660251 0. 0.\n", + " 0. 0. 0. 0. ]\n", + "norm: 1.000000093060037\n" + ] + } + ], + "execution_count": 57 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:54.098107Z", + "start_time": "2024-07-18T23:01:54.094975Z" + } + }, + "cell_type": "code", + "source": [ + "from qulearn.qlayer import HatBasisQFE\n", + "\n", + "embed = HatBasisQFE(wires=2*num_qubits, basis=hat_basis, sqrt=True, normalize=False)\n", + "\n", + "x = torch.tensor([-1.1])\n", + "norm = embed.compute_norm(x)\n", + "print(norm)\n", + "pos = hat_basis.position(x)\n", + "a, b = hat_basis.nonz_vals(x)\n", + "print(a, b)\n", + "print(pos)" + ], + "id": "9130686d5d3a9c72", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9219543933868408\n", + "tensor([0.1500]) tensor([0.8500])\n", + "tensor([-1.])\n" + ] + } + ], + "execution_count": 58 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:54.134150Z", + "start_time": "2024-07-18T23:01:54.098621Z" + } + }, + "cell_type": "code", + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "\n", + "num_pnts = 50\n", + "\n", + "# Generate a grid of x and y values\n", + "x = torch.linspace(-0.99, 0.99, num_pnts)\n", + "y = torch.linspace(-0.99, 0.99, num_pnts)\n", + "X, Y = torch.meshgrid(x, y, indexing='xy')\n", + "Z = torch.empty(num_pnts, num_pnts)\n", + "\n", + "# Evaluate the circuit at each point in the grid and extract the j-th component\n", + "idx = 1\n", + "for i in range(num_pnts):\n", + " for k in range(num_pnts):\n", + " xy = torch.tensor([X[i, k], Y[i, k]], dtype=torch.float32)\n", + " out = circuit(xy)[idx]\n", + " Z[i, k] = torch.tensor(out)\n", + "# Convert tensors to numpy arrays for plotting\n", + "X = X.numpy()\n", + "Y = Y.numpy()\n", + "Z = Z.numpy()\n", + "\n", + "# Create a 2D heatmap plot\n", + "plt.figure(figsize=(10, 6))\n", + "plt.imshow(Z, extent=[-1, 1, -1, 1], origin='lower', cmap='viridis', aspect='auto')\n", + "\n", + "# Add labels and title\n", + "plt.xlabel('$x$')\n", + "plt.ylabel('$y$')\n", + "plt.title(f\"$\\\\varphi_j$\")\n", + "\n", + "# Add a color bar which maps values to colors\n", + "plt.colorbar(label=f'$\\\\varphi_j$')\n", + "\n", + "# Save the figure\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "id": "8f41ff534081649d", + "outputs": [ + { + "ename": "ValueError", + "evalue": "only one element tensors can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[59], line 17\u001B[0m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(num_pnts):\n\u001B[1;32m 16\u001B[0m xy \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mtensor([X[i, k], Y[i, k]], dtype\u001B[38;5;241m=\u001B[39mtorch\u001B[38;5;241m.\u001B[39mfloat32)\n\u001B[0;32m---> 17\u001B[0m out \u001B[38;5;241m=\u001B[39m \u001B[43mcircuit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxy\u001B[49m\u001B[43m)\u001B[49m[idx]\n\u001B[1;32m 18\u001B[0m Z[i, k] \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mtensor(out)\n\u001B[1;32m 19\u001B[0m \u001B[38;5;66;03m# Convert tensors to numpy arrays for plotting\u001B[39;00m\n", + "File \u001B[0;32m~/Projects/QuLearn/.venv/lib/python3.11/site-packages/pennylane/qnode.py:800\u001B[0m, in \u001B[0;36mQNode.__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 797\u001B[0m set_shots(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_original_device, override_shots)(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_update_gradient_fn)()\n\u001B[1;32m 799\u001B[0m \u001B[38;5;66;03m# construct the tape\u001B[39;00m\n\u001B[0;32m--> 800\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconstruct\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 802\u001B[0m cache \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexecute_kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcache\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m 803\u001B[0m using_custom_cache \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 804\u001B[0m \u001B[38;5;28mhasattr\u001B[39m(cache, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m__getitem__\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 805\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(cache, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m__setitem__\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 806\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(cache, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m__delitem__\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 807\u001B[0m )\n", + "File \u001B[0;32m~/Projects/QuLearn/.venv/lib/python3.11/site-packages/pennylane/qnode.py:711\u001B[0m, in \u001B[0;36mQNode.construct\u001B[0;34m(self, args, kwargs)\u001B[0m\n\u001B[1;32m 708\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mconstruct\u001B[39m(\u001B[38;5;28mself\u001B[39m, args, kwargs):\n\u001B[1;32m 709\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Call the quantum function with a tape context, ensuring the operations get queued.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 711\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_tape \u001B[38;5;241m=\u001B[39m \u001B[43mmake_qscript\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunc\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 712\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_tape\u001B[38;5;241m.\u001B[39m_queue_category \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_ops\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 713\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_qfunc_output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtape\u001B[38;5;241m.\u001B[39m_qfunc_output\n", + "File \u001B[0;32m~/Projects/QuLearn/.venv/lib/python3.11/site-packages/pennylane/tape/qscript.py:1346\u001B[0m, in \u001B[0;36mmake_qscript..wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 1344\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapper\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m 1345\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m AnnotatedQueue() \u001B[38;5;28;01mas\u001B[39;00m q:\n\u001B[0;32m-> 1346\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1348\u001B[0m qscript \u001B[38;5;241m=\u001B[39m QuantumScript\u001B[38;5;241m.\u001B[39mfrom_queue(q)\n\u001B[1;32m 1349\u001B[0m qscript\u001B[38;5;241m.\u001B[39m_qfunc_output \u001B[38;5;241m=\u001B[39m result\n", + "Cell \u001B[0;32mIn[57], line 14\u001B[0m, in \u001B[0;36mcircuit\u001B[0;34m(x)\u001B[0m\n\u001B[1;32m 12\u001B[0m \u001B[38;5;129m@qml\u001B[39m\u001B[38;5;241m.\u001B[39mqnode(dev)\n\u001B[1;32m 13\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcircuit\u001B[39m(x):\n\u001B[0;32m---> 14\u001B[0m \u001B[43membed\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcircuit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m qml\u001B[38;5;241m.\u001B[39mstate()\n", + "File \u001B[0;32m~/Projects/QuLearn/qulearn/qlayer.py:156\u001B[0m, in \u001B[0;36mHatBasisQFE.circuit\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcircuit\u001B[39m(\u001B[38;5;28mself\u001B[39m, x: Tensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 149\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 150\u001B[0m \u001B[38;5;124;03m Define the quantum circuit for this layer.\u001B[39;00m\n\u001B[1;32m 151\u001B[0m \n\u001B[1;32m 152\u001B[0m \u001B[38;5;124;03m :param x: Input tensor that is passed to the quantum circuit.\u001B[39;00m\n\u001B[1;32m 153\u001B[0m \u001B[38;5;124;03m :type x: Tensor\u001B[39;00m\n\u001B[1;32m 154\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 156\u001B[0m position \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mint\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbasis\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mposition\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 157\u001B[0m a, b \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbasis\u001B[38;5;241m.\u001B[39mnonz_vals(x)\n\u001B[1;32m 159\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msqrt:\n\u001B[1;32m 160\u001B[0m \u001B[38;5;66;03m# sometimes the values are close to 0 and negative\u001B[39;00m\n", + "\u001B[0;31mValueError\u001B[0m: only one element tensors can be converted to Python scalars" + ] + } + ], + "execution_count": 59 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-18T23:01:54.134750Z", + "start_time": "2024-07-18T23:01:54.134710Z" + } + }, + "cell_type": "code", + "source": [ + "a = 0.3\n", + "b = 0.5\n", + "\n", + "def lambda1(beta1, beta2):\n", + " nom = (b-a)*(beta1-beta2)\n", + " denom = (b-a-1)*beta2-(b-a)*beta1\n", + " \n", + " return nom/denom\n", + "\n", + "def lambda2(beta1, beta2):\n", + " nom = (b-a-1)*(beta1-beta2)\n", + " denom = (b-a-1)*beta2-(b-a)*beta1\n", + " \n", + " return nom/denom\n", + "\n", + "def effbeta(beta1, beta2):\n", + " lam1 = lambda1(beta1, beta2)\n", + " lam2 = lambda2(beta1, beta2)\n", + " \n", + " beta = beta1*(lam1+1)*(1+a-b)+beta2*(b-a)*(lam2+1)\n", + " return beta" + ], + "id": "3d14fb660b4bb878", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Create a meshgrid for the range of beta1 and beta2 values\n", + "n1 = 50\n", + "n2 = 50\n", + "beta1_range = np.linspace(0.05, 400, n1)\n", + "beta2_range = np.linspace(0.05, 400, n2)\n", + "beta1, beta2 = np.meshgrid(beta1_range, beta2_range)\n", + "\n", + "# Compute effbeta for each combination of beta1 and beta2\n", + "eff_beta_values = effbeta(beta1, beta2)\n", + "\n", + "# Create a heat plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.imshow(eff_beta_values, extent=(0.05, 400, 0.05, 400), origin='lower', aspect='auto')\n", + "plt.colorbar(label='effbeta')\n", + "plt.xlabel('beta1')\n", + "plt.ylabel('beta2')\n", + "plt.title('Heat Plot of effbeta')\n", + "plt.show()\n", + "\n", + "# Create a surface plot\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "surf = ax.plot_surface(beta1, beta2, eff_beta_values, cmap='viridis')\n", + "fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)\n", + "ax.set_xlabel('beta1')\n", + "ax.set_ylabel('beta2')\n", + "ax.set_zlabel('effbeta')\n", + "ax.set_title('Surface Plot of effbeta')\n", + "plt.show()" + ], + "id": "3bb2b8e7616244d9", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from torch.utils.data import DataLoader, TensorDataset, random_split\n", + "# Normalize the data to [-1, 1]\n", + "beta1_min, beta1_max = beta1.min(), beta1.max()\n", + "beta2_min, beta2_max = beta2.min(), beta2.max()\n", + "eff_beta_min, eff_beta_max = eff_beta_values.min(), eff_beta_values.max()\n", + "\n", + "beta1_normalized = 2 * (beta1 - beta1_min) / (beta1_max - beta1_min) - 1\n", + "beta2_normalized = 2 * (beta2 - beta2_min) / (beta2_max - beta2_min) - 1\n", + "eff_beta_normalized = 2 * (eff_beta_values - eff_beta_min) / (eff_beta_max - eff_beta_min) - 1\n", + "\n", + "# Flatten the arrays and combine them\n", + "beta1_flat = beta1_normalized.flatten()\n", + "beta2_flat = beta2_normalized.flatten()\n", + "eff_beta_flat = eff_beta_normalized.flatten()\n", + "\n", + "# Convert to PyTorch tensors\n", + "inputs = torch.tensor(np.vstack((beta1_flat, beta2_flat)).T, dtype=torch.float32)\n", + "outputs = torch.tensor(eff_beta_flat, dtype=torch.float32).unsqueeze(1)\n", + "\n", + "# Create a dataset and split into training and validation sets\n", + "dataset = TensorDataset(inputs, outputs)\n", + "train_size = int(0.8 * len(dataset))\n", + "val_size = len(dataset) - train_size\n", + "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n", + "\n", + "# Create dataloaders\n", + "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n", + "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n", + "\n", + "# Plot the normalized eff_beta values as a heat plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.imshow(eff_beta_normalized.reshape(beta1.shape), extent=(-1., 1., -1., 1.), origin='lower', aspect='auto')\n", + "plt.colorbar(label='Normalized effbeta')\n", + "plt.xlabel('beta1')\n", + "plt.ylabel('beta2')\n", + "plt.title('Heat Plot of Normalized effbeta')\n", + "plt.show()\n", + "\n", + "# Plot the normalized eff_beta values as a surface plot\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "surf = ax.plot_surface(beta1_normalized, beta2_normalized, eff_beta_normalized, cmap='viridis')\n", + "fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)\n", + "ax.set_xlabel('beta1')\n", + "ax.set_ylabel('beta2')\n", + "ax.set_zlabel('Normalized effbeta')\n", + "ax.set_title('Surface Plot of Normalized effbeta')\n", + "plt.show()" + ], + "id": "b74ce22a367906f2", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qulearn.qlayer import ParallelIQPEncoding, AltRotCXLayer, HamiltonianLayer\n", + "num_features = 2\n", + "num_qubits = 6\n", + "base = 3.0\n", + "omega = 1.0\n", + "embed = ParallelIQPEncoding(wires=num_qubits,\n", + " num_features=num_features,\n", + " n_repeat=1,\n", + " base=base,\n", + " omega=omega)\n", + "n_layers = 1\n", + "var = AltRotCXLayer(wires=num_qubits, n_layers=n_layers)\n", + "\n", + "obs = [qml.Identity(0), qml.PauliZ(0)]\n", + "model = HamiltonianLayer(embed, var, observables=obs)\n", + "drawer = qml.draw(model.qnode, show_all_wires=True, expansion_strategy=\"device\")\n", + "x = torch.tensor([1.0, 2.0])\n", + "print(drawer(x))\n", + "nump = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "print(\"Number of parameters: \", nump)" + ], + "id": "7453d8e6508e91c0", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Define the number of points in each dimension\n", + "num_pnts = 50\n", + "\n", + "# Generate a grid of x and y values\n", + "x = torch.linspace(-0.99, 0.99, num_pnts)\n", + "y = torch.linspace(-0.99, 0.99, num_pnts)\n", + "X, Y = torch.meshgrid(x, y)\n", + "Z = torch.empty(num_pnts, num_pnts)\n", + "\n", + "# Evaluate the model at each point in the grid\n", + "for i in range(num_pnts):\n", + " for j in range(num_pnts):\n", + " xy = torch.tensor([X[i, j], Y[i, j]])\n", + " Z[i, j] = model(xy).item()\n", + "\n", + "# Convert tensors to numpy arrays for plotting\n", + "X = X.numpy()\n", + "Y = Y.numpy()\n", + "Z = Z.numpy()\n", + "\n", + "# Create a 3D surface plot\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "surf = ax.plot_surface(X, Y, Z, cmap='viridis')\n", + "\n", + "# Add labels and title\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$')\n", + "ax.set_zlabel('$z$')\n", + "ax.set_title(\"$\\langle Z_0\\\\rangle$\")\n", + "\n", + "ax.view_init(elev=30, azim=45)\n", + "# Add a color bar which maps values to colors\n", + "fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)\n", + "\n", + "# Save the figure\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "id": "bb0c58697d65985e", + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/test_datagen.py b/tests/test_datagen.py index b28ff7b..bd7ca35 100644 --- a/tests/test_datagen.py +++ b/tests/test_datagen.py @@ -1,15 +1,16 @@ -import pytest -import torch import math + import numpy as np +import pytest import torch from torch.utils.data import DataLoader + from qulearn.datagen import ( DataGenCapacity, DataGenFat, DataGenRademacher, - UniformPrior, NormalPrior, + UniformPrior, generate_lhs_samples, generate_model_lhs_samples, ) @@ -258,9 +259,7 @@ def test_generate_model_lhs_samples(): assert len(parameter_samples) == n_samples for sample in parameter_samples: assert isinstance(sample, list) - assert len(sample) == len( - list(filter(lambda p: p.requires_grad, model.parameters())) - ) + assert len(sample) == len(list(filter(lambda p: p.requires_grad, model.parameters()))) for param in sample: assert isinstance(param, torch.Tensor) assert (param.detach().numpy() >= lower_bound).all() diff --git a/tests/test_fat.py b/tests/test_fat.py index 4306a0b..f383c44 100644 --- a/tests/test_fat.py +++ b/tests/test_fat.py @@ -1,11 +1,9 @@ -import os -import tempfile import torch from torch.nn import Linear from torch.optim import Adam -from qulearn.fat import fat_shattering_dim, check_shattering, normalize_const from qulearn.datagen import DataGenFat, UniformPrior +from qulearn.fat import check_shattering, fat_shattering_dim, normalize_const from qulearn.trainer import SupervisedTrainer @@ -54,9 +52,7 @@ def test_fat_shattering_dim(): metrics = {"Loss": loss_fn} trainer = SupervisedTrainer(opt, loss_fn=loss_fn, metrics=metrics, num_epochs=100) - fat_shattering_dimension = fat_shattering_dim( - model, datagen, trainer, dmin, dmax, gamma - ) + fat_shattering_dimension = fat_shattering_dim(model, datagen, trainer, dmin, dmax, gamma) assert isinstance(fat_shattering_dimension, int) assert fat_shattering_dimension > 0 @@ -79,9 +75,7 @@ def test_linear_model(): metrics = {"Loss": loss_fn} trainer = SupervisedTrainer(opt, loss_fn=loss_fn, metrics=metrics, num_epochs=100) - fat_shattering_dimension = fat_shattering_dim( - model, datagen, trainer, dmin, dmax, gamma - ) + fat_shattering_dimension = fat_shattering_dim(model, datagen, trainer, dmin, dmax, gamma) assert isinstance(fat_shattering_dimension, int) assert fat_shattering_dimension >= sizex diff --git a/tests/test_fim.py b/tests/test_fim.py index 709b5ed..de93b47 100644 --- a/tests/test_fim.py +++ b/tests/test_fim.py @@ -1,24 +1,24 @@ -import pytest import math -import torch + import numpy as np +import pytest +import torch + from qulearn.fim import ( - empirical_fim, + compute_effdim, compute_fims, - mc_integrate_fim_trace, - norm_const_fim, const_effdim, + empirical_fim, half_log_det, + mc_integrate_fim_trace, mc_integrate_fims_effdim, - compute_effdim, + norm_const_fim, ) @pytest.fixture def model(): - model = torch.nn.Sequential( - torch.nn.Linear(1, 2, bias=False), torch.nn.Softmax(dim=1) - ) + model = torch.nn.Sequential(torch.nn.Linear(1, 2, bias=False), torch.nn.Softmax(dim=1)) # Manually set weights to known values model[0].weight.data = torch.tensor([[1.0], [1.0]]) num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) @@ -144,9 +144,7 @@ def test_norm_const(): @pytest.fixture def setup_effdim(): - model = torch.nn.Sequential( - torch.nn.Linear(1, 2, bias=False), torch.nn.Softmax(dim=1) - ) + model = torch.nn.Sequential(torch.nn.Linear(1, 2, bias=False), torch.nn.Softmax(dim=1)) model[0].weight.data = torch.tensor([[1.0], [1.0]]) X = torch.tensor([[1.0], [2.0]]) @@ -160,9 +158,7 @@ def setup_effdim(): @pytest.fixture def setup_effdim2(): dim = 3 - model = torch.nn.Sequential( - torch.nn.Linear(dim, 5, bias=True), torch.nn.Softmax(dim=1) - ) + model = torch.nn.Sequential(torch.nn.Linear(dim, 5, bias=True), torch.nn.Softmax(dim=1)) n = 50 X = torch.randn((n, dim)) @@ -176,9 +172,7 @@ def setup_effdim2(): def test_comp_effdim(setup_effdim): effdim = compute_effdim(*setup_effdim) - num_parameters = sum( - p.numel() for p in setup_effdim[0].parameters() if p.requires_grad - ) + num_parameters = sum(p.numel() for p in setup_effdim[0].parameters() if p.requires_grad) X = setup_effdim[1] diag = 0.25 * X[0][0] ** 2 + 0.25 * X[1][0] ** 2 diag *= 0.5 @@ -188,9 +182,7 @@ def test_comp_effdim(setup_effdim): normconst = setup_effdim[4] * num_parameters / traceint n = len(X) const = setup_effdim[5] * n / (2 * math.pi * math.log(n)) - sqrtdet = math.sqrt( - torch.det(torch.eye(num_parameters) + const * normconst * expected_fim) - ) + sqrtdet = math.sqrt(torch.det(torch.eye(num_parameters) + const * normconst * expected_fim)) dgamma = 2 * math.log(sqrtdet) / math.log(const) diff --git a/tests/test_hat_basis.py b/tests/test_hat_basis.py index cd04422..61b474f 100644 --- a/tests/test_hat_basis.py +++ b/tests/test_hat_basis.py @@ -1,4 +1,5 @@ import torch + from qulearn.hat_basis import HatBasis @@ -6,27 +7,21 @@ def test_position_left_of_a(): hat_basis = HatBasis(a=0.0, b=1.0, num_nodes=5) x = torch.tensor([-0.1, -1.0]) expected = torch.tensor([-1, -1]) - assert torch.equal( - hat_basis.position(x), expected - ), "Position left of a should be -1" + assert torch.equal(hat_basis.position(x), expected), "Position left of a should be -1" def test_position_right_of_b(): hat_basis = HatBasis(a=0.0, b=1.0, num_nodes=5) x = torch.tensor([1.1, 2.0]) expected = torch.tensor([-2, -2]) - assert torch.equal( - hat_basis.position(x), expected - ), "Position right of b should be -2" + assert torch.equal(hat_basis.position(x), expected), "Position right of b should be -2" def test_position_within_range(): hat_basis = HatBasis(a=0.0, b=1.0, num_nodes=5) x = torch.tensor([0.25, 0.5, 0.75]) expected = torch.tensor([1, 2, 3]) - assert torch.equal( - hat_basis.position(x), expected - ), "Position within range should be correct" + assert torch.equal(hat_basis.position(x), expected), "Position within range should be correct" def test_grid_points_boundary_conditions(): diff --git a/tests/test_loss.py b/tests/test_loss.py index aaa2ab6..2a42230 100644 --- a/tests/test_loss.py +++ b/tests/test_loss.py @@ -1,5 +1,6 @@ import pytest import torch + from qulearn.loss import RademacherLoss diff --git a/tests/test_memory.py b/tests/test_memory.py index d042ff1..c0028db 100644 --- a/tests/test_memory.py +++ b/tests/test_memory.py @@ -1,13 +1,13 @@ -import os -import tempfile +from typing import List + import torch from torch.nn import Linear from torch.optim import Adam -from typing import List -from qulearn.qlayer import IQPEmbeddingLayer, RYCZLayer, HamiltonianLayer -from qulearn.observable import parities_all_observables -from qulearn.memory import memory, fit_rand_labels + from qulearn.datagen import DataGenCapacity +from qulearn.memory import fit_rand_labels, memory +from qulearn.observable import parities_all_observables +from qulearn.qlayer import HamiltonianLayer, IQPEmbeddingLayer, RYCZLayer from qulearn.trainer import SupervisedTrainer diff --git a/tests/test_mps.py b/tests/test_mps.py index 9588e87..2e2416f 100644 --- a/tests/test_mps.py +++ b/tests/test_mps.py @@ -1,23 +1,20 @@ import pytest -import torch import tntorch +import torch + +from qulearn.hat_basis import HatBasis from qulearn.mps import ( - MPSQGates, HatBasisMPS, + MPSQGates, compute_max_rank_power, embed2unitary, zerobit_position_odd, ) -from qulearn.hat_basis import HatBasis @pytest.fixture def sample_mps(): - cores = ( - [torch.rand(1, 2, 2)] - + [torch.rand(2, 2, 2) for _ in range(2)] - + [torch.rand(2, 2, 1)] - ) + cores = [torch.rand(1, 2, 2)] + [torch.rand(2, 2, 2) for _ in range(2)] + [torch.rand(2, 2, 1)] mps = tntorch.Tensor(cores) return mps @@ -34,7 +31,7 @@ def test_compute_max_rank_power(sample_mps): def test_embed2unitary(): A = torch.rand(4, 2) - Q, _ = torch.qr(A) + Q, _ = torch.linalg.qr(A) U = embed2unitary(Q) assert torch.allclose(U @ U.T, torch.eye(U.shape[0]), atol=1e-6) diff --git a/tests/test_mps_kronprod.py b/tests/test_mps_kronprod.py new file mode 100644 index 0000000..5affaa9 --- /dev/null +++ b/tests/test_mps_kronprod.py @@ -0,0 +1,49 @@ +import numpy as np +import pytest +import tntorch as tn + +from qulearn.mps_kronprod import kron, zkron + + +def tensor_to_vector(tensor): + return tensor.numpy().reshape(-1) + + +def test_kron(): + t1 = tn.randn([2] * 3) + t2 = tn.ones([2] * 3) + T1 = tensor_to_vector(t1) + T2 = tensor_to_vector(t2) + + t3 = kron(t1, t2) + T3 = tensor_to_vector(t3) + + T3_expected = np.kron(T1, T2) + delta = np.linalg.norm(T3_expected - T3) + + assert delta < 1e-5, f"Delta too large: {delta}" + + +def test_zkron(): + t1 = tn.randn([2] * 3) + t2 = tn.ones([2] * 3) + + t4 = zkron(t1, t2) + T4 = tensor_to_vector(t4) + + # Assuming zkron2 is an alternative implementation of zkron for comparison + # If zkron2 does not exist, replace this part with an appropriate test + t5 = zkron(t1, t2) # Replace zkron with zkron2 if available + T5 = tensor_to_vector(t5) + + delta = np.linalg.norm(T4 - T5) + + assert delta < 1e-5, f"Delta too large: {delta}" + + +def test_core_length_mismatch(): + t1 = tn.randn([2] * 3) + t2 = tn.randn([2] * 4) # Different size to induce error + + with pytest.raises(ValueError): + zkron(t1, t2) diff --git a/tests/test_observable.py b/tests/test_observable.py index 87de194..3c23ba5 100644 --- a/tests/test_observable.py +++ b/tests/test_observable.py @@ -1,10 +1,12 @@ -import pytest from itertools import combinations -import torch + import pennylane as qml +import pytest +import torch + from qulearn.observable import ( - parity_all_hamiltonian, parities_all_observables, + parity_all_hamiltonian, sequence2parity_observable, ) diff --git a/tests/test_qkernel.py b/tests/test_qkernel.py index 22df6f0..edf4ffb 100644 --- a/tests/test_qkernel.py +++ b/tests/test_qkernel.py @@ -1,9 +1,9 @@ +import pennylane as qml import pytest import torch -import pennylane as qml -from qulearn.qlayer import HadamardLayer from qulearn.qkernel import QKernel +from qulearn.qlayer import HadamardLayer DEFAULT_QDEV_CFG = {"name": "default.qubit", "wires": 2, "shots": None} diff --git a/tests/test_qlayer.py b/tests/test_qlayer.py index 370f10f..7a7135c 100644 --- a/tests/test_qlayer.py +++ b/tests/test_qlayer.py @@ -1,25 +1,26 @@ -import torch -import pytest import pennylane as qml +import pytest +import torch + +from qulearn.hat_basis import HatBasis from qulearn.qlayer import ( - MeasurementType, + AltRotCXLayer, CircuitLayer, - MeasurementLayer, - IQPEmbeddingLayer, - HatBasisQFE, - TwoQubitRotCXMPSLayer, EmbedU, - RYCZLayer, - AltRotCXLayer, - IQPERYCZLayer, - IQPEAltRotCXLayer, - HamiltonianLayer, HadamardLayer, - ParallelIQPEncoding, + HamiltonianLayer, + HatBasisQFE, + IQPEAltRotCXLayer, + IQPEmbeddingLayer, + IQPERYCZLayer, + Linear2DBasisQFE, + MeasurementLayer, + MeasurementType, ParallelEntangledIQPEncoding, + ParallelIQPEncoding, + RYCZLayer, + TwoQubitRotCXMPSLayer, ) -from qulearn.hat_basis import HatBasis - # Unit tests for CircuitLayer class @@ -211,7 +212,6 @@ def test_embed_altrotvar_layer_circuit(mock_embed_altrotvar_layer): def test_embed_ryczvar_layer_num_parameters(mock_embed_ryczvar_layer): layer = mock_embed_ryczvar_layer - x = torch.tensor([0.1, 0.2]) num_parameters = sum(p.numel() for p in layer.parameters()) num_qubits = len(layer.wires) expected = layer.num_repeat * (num_qubits + 2 * (num_qubits - 1)) @@ -220,7 +220,6 @@ def test_embed_ryczvar_layer_num_parameters(mock_embed_ryczvar_layer): def test_embed_altrotvar_layer_num_parameters(mock_embed_altrotvar_layer): layer = mock_embed_altrotvar_layer - x = torch.tensor([0.1, 0.2]) num_parameters = sum(p.numel() for p in layer.parameters()) num_qubits = len(layer.wires) expected = layer.num_repeat * 3 * (num_qubits + 2 * (num_qubits - 1)) @@ -303,9 +302,7 @@ def circuit(): return qml.probs(wires=wires) probs = circuit() - assert 0.125 == pytest.approx( - probs - ) # Should be equal probabilities for all 8 states + assert 0.125 == pytest.approx(probs) # Should be equal probabilities for all 8 states def test_parallel_iqp_encoding(): @@ -442,9 +439,7 @@ def sample_hat_basis(): def test_hat_basis_qfe_initialization(sample_hat_basis): - hat_basis_qfe = HatBasisQFE( - wires=2, basis=sample_hat_basis, sqrt=True, normalize=True - ) + hat_basis_qfe = HatBasisQFE(wires=2, basis=sample_hat_basis, sqrt=True, normalize=True) assert hat_basis_qfe.sqrt is True assert hat_basis_qfe.normalize is True @@ -466,6 +461,27 @@ def test_hat_basis_qfe_compute_norm(sample_hat_basis): assert 1.0 == pytest.approx(norm, abs=1e-4) +def test_Linear2DBasisQFE_initialization(sample_hat_basis): + layer = Linear2DBasisQFE(wires=2, basis=sample_hat_basis, sqrt=True, normalize=True) + assert layer.sqrt is True + assert layer.normalize is True + + +def test_Linear2DBasisQFE_circuit(sample_hat_basis): + x = torch.tensor([0.0, 0.0]) + layer = Linear2DBasisQFE(wires=4, basis=sample_hat_basis) + layer.circuit(x) + assert layer.norm == pytest.approx(1.0, abs=1e-4) + + +def test_Linear2DBasisQFE_compute_norm(sample_hat_basis): + x = torch.tensor([0.0, 0.0]) + layer = Linear2DBasisQFE(wires=4, basis=sample_hat_basis) + norm = layer.compute_norm(x) + assert isinstance(norm, float) + assert 1.0 == pytest.approx(norm, abs=1e-4) + + def test_TwoQubitRotCXMPSLayer_initialization(): wires = 4 layer = TwoQubitRotCXMPSLayer(wires=wires) diff --git a/tests/test_rademacher.py b/tests/test_rademacher.py index 6383328..dfcc75d 100644 --- a/tests/test_rademacher.py +++ b/tests/test_rademacher.py @@ -1,13 +1,14 @@ import logging import math + import torch from torch.nn import Linear from torch.optim import Adam +from qulearn.datagen import DataGenRademacher, NormalPrior +from qulearn.loss import RademacherLoss from qulearn.rademacher import rademacher from qulearn.trainer import SupervisedTrainer -from qulearn.loss import RademacherLoss -from qulearn.datagen import NormalPrior, DataGenRademacher def test_rademacher(): diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 17240c3..4282eba 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -1,25 +1,28 @@ -import os import io -import pytest +import logging +import os import tempfile + +import pytest import torch -import logging -from torch.utils.tensorboard import SummaryWriter -from torch.utils.data import DataLoader, TensorDataset +from torch.nn import MSELoss from torch.optim import Adam -from qulearn.trainer import SupervisedTrainer, RidgeRegression +from torch.utils.data import DataLoader, TensorDataset +from torch.utils.tensorboard import SummaryWriter + from qulearn.qkernel import QKernel from qulearn.qlayer import HadamardLayer, ParallelEntangledIQPEncoding -from torch.nn import MSELoss +from qulearn.trainer import RidgeRegression, SupervisedTrainer def test_trainer(): # Create a sample input dataset X and corresponding labels Y N = 104 - X = torch.randn(N, 10, dtype=torch.float64) - A = torch.randn(10, 1, dtype=torch.float64) - eps = torch.randn(N, dtype=torch.float64) * 0.01 - b = torch.randn(1, dtype=torch.float64) + d = 10 + X = torch.randn(N, d, dtype=torch.float64) + A = torch.randn(d, 1, dtype=torch.float64) + eps = torch.randn(N, 1, dtype=torch.float64) * 0.01 + b = torch.randn(1, dtype=torch.float64) * torch.ones(N, 1, dtype=torch.float64) Y = torch.matmul(X, A) + b + eps model = torch.nn.Linear(10, 1, bias=True, dtype=torch.float64) @@ -61,7 +64,7 @@ def setup_ridge_regression(): embed = HadamardLayer(wires=2) X_train = torch.Tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) - labels = torch.ones(3, 1) + labels = torch.ones(3) train_data = TensorDataset(X_train, labels) valid_data = TensorDataset(X_train, labels) train_data = DataLoader(train_data, batch_size=3) @@ -120,7 +123,7 @@ def run_training(): num_features = 1 num_samples = 10 X_train = torch.randn((num_samples, num_features)) - labels = torch.randn((num_samples, 1)) + labels = torch.randn((num_samples)) model = QKernel(embed, X_train) predicted = model(X_train) @@ -160,6 +163,4 @@ def test_training_behavior(): ), f"Loss did not decrease after training. Before: {loss_before}, After: {loss_after}" assert "Train - Metrics: mse_loss:" in logs, "Train logging missing or incorrect" - assert ( - "Validate - Metrics: mse_loss:" in logs - ), "Validation logging missing or incorrect" + assert "Validate - Metrics: mse_loss:" in logs, "Validation logging missing or incorrect" diff --git a/tests/test_utils.py b/tests/test_utils.py index 4c930ea..0a6edc1 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,12 +1,14 @@ +from collections import Counter + import pennylane as qml import torch -from collections import Counter + from qulearn.utils import ( - probabilities_to_dictionary, - samples_to_dictionary, all_bin_sequences, parities_outcome, parities_outcome_probs, + probabilities_to_dictionary, + samples_to_dictionary, ) @@ -24,9 +26,7 @@ def test_samples_to_dictionary(): # all_bin_sequences def test_all_bin_sequences(): - assert Counter(map(tuple, all_bin_sequences(2))) == Counter( - map(tuple, [[0, 1], [1], [0], []]) - ) + assert Counter(map(tuple, all_bin_sequences(2))) == Counter(map(tuple, [[0, 1], [1], [0], []])) # parities_outcome