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Config loader

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Code using omegaconf to handle IO.

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IO

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source

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class_to_str

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 class_to_str (cls)
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source

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load_config

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 load_config (file_path)
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source

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config_to_dict

+
+
 config_to_dict (config)
+
+
+

source

+
+
+

save_dataclass_yaml

+
+
 save_dataclass_yaml (data_obj, file_path)
+
+
+

source

+
+
+

save_dict_yaml

+
+
 save_dict_yaml (dict_obj, file_path)
+
+

Test

+
+
@dataclass
+class MyConfig:    
+    target:str = class_to_str(OmegaConf)
+    clr_dim: int = 80
+    features: list[int]=None
+    
+c = MyConfig()
+c.features = [1,2,3]
+
+OmegaConf.structured(c)
+
+
{'target': 'omegaconf.omegaconf.OmegaConf', 'clr_dim': 80, 'features': [1, 2, 3]}
+
+
+
+
+
+

Object config load

+

Mostly taken from: https://github.com/Stability-AI/stablediffusion

+
+

source

+
+

get_obj_from_str

+
+
 get_obj_from_str (string, reload=False)
+
+
+

source

+
+
+

instantiate_from_config

+
+
 instantiate_from_config (config)
+
+
+

source

+
+
+

load_model_from_config

+
+
 load_model_from_config (config, ckpt, device)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/dataset/cached_qc_dataset.html b/dataset/cached_qc_dataset.html new file mode 100644 index 0000000..3952ec9 --- /dev/null +++ b/dataset/cached_qc_dataset.html @@ -0,0 +1,723 @@ + + + + + + + + + +genQC - Cached quantum circuit dataset + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Cached quantum circuit dataset

+
+ + + +
+ + + + +
+ + +
+ + +

Quantum circuit dataset that caches the y prompts using the CLIP encoder. This speeds up training significantly!

+
+

source

+
+

Cached_OpenClip_Dataset

+
+
 Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),
+                          **parameters)
+
+

Adds .caching to the Quantum circuit dataset class.

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/dataset/config_dataset.html b/dataset/config_dataset.html new file mode 100644 index 0000000..8e819fe --- /dev/null +++ b/dataset/config_dataset.html @@ -0,0 +1,731 @@ + + + + + + + + + +genQC - Config dataset + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Config dataset

+
+ + + +
+ + + + +
+ + +
+ + +
+

source

+
+

Config_Dataset_config

+
+
 Config_Dataset_config (store_dict:dict)
+
+

Config dataclass used for storage.

+
+

source

+
+
+

Config_Dataset

+
+
 Config_Dataset (device:torch.device=device(type='cpu'), **parameters)
+
+

Base class for datasets, manages loading and saving.

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/dataset/dataset_helper.html b/dataset/dataset_helper.html new file mode 100644 index 0000000..d888f10 --- /dev/null +++ b/dataset/dataset_helper.html @@ -0,0 +1,839 @@ + + + + + + + + + +genQC - Dataset helper functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Dataset helper functions

+
+ + + +
+ + + + +
+ + +
+ + +

Some comonly used functions for datasets.

+
+

Checking

+
+

source

+
+

check_duplicate_in_dataset

+
+
 check_duplicate_in_dataset (x, dataset)
+
+

Check if ‘x’ is in ‘dataset’

+
+

source

+
+
+

check_duplicates_in_dataset

+
+
 check_duplicates_in_dataset (xs, dataset, return_ind=False, invert=False)
+
+

Checks if xs is are dataset. Boolean invert changes if we count duplicates (False) or ones that are not in dataset (True). Uses torch.vmap which copies dataset for every element in xs.

+

Check if this works:

+
+
xs = torch.tensor(
+    [[0.7, 1, 0.5], 
+     [0.3, 1, 0.5],
+     [  0, 1, 0.5]])
+
+d = torch.tensor([
+    [0.11, 1, 0.5],
+    [0.70, 1, 0.5],      #here a dup
+    [0.71, 1, 0.5],
+    [0.3 , 1, 0.5]])
+
+check_duplicates_in_dataset(xs, d, return_ind=True)
+
+
(2, tensor([0, 1]))
+
+
+
+
+
+

Manipulating

+
+

source

+
+

shuffle_tensor_dataset

+
+
 shuffle_tensor_dataset (x, y=None, *z)
+
+

Assumes numpy or tensor objects with same length.

+
+

source

+
+
+

get_unique_elements_indices

+
+
 get_unique_elements_indices (tensor)
+
+

Returns indices of unique_elements in tensor.

+
+

source

+
+
+

uniquify_tensor_dataset

+
+
 uniquify_tensor_dataset (x, y=None, *z)
+
+

x has to be tensor, assumes numpy or tensor obj for y and z

+
+

source

+
+
+

balance_tensor_dataset

+
+
 balance_tensor_dataset (x, y, *z, samples:int=None,
+                         make_unique:bool=True, y_uniques=None,
+                         shuffle_lables:bool=True, add_balance_fn:<built-
+                         infunctioncallable>=None)
+
+

Assumes x is tensor and y is tensor or numpy.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/dataset/mixed_cached_qc_dataset.html b/dataset/mixed_cached_qc_dataset.html new file mode 100644 index 0000000..c2c14aa --- /dev/null +++ b/dataset/mixed_cached_qc_dataset.html @@ -0,0 +1,739 @@ + + + + + + + + + +genQC - Mixed cached dataset + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Mixed cached dataset

+
+ + + +
+ + + + +
+ + +
+ + +

Dataset that combines and handles multiple cached datasets, e.g. for multiple qubits. Here we also handle paddings.

+
+

source

+
+

Mixed_Cached_OpenClip_Dataset_config

+
+
 Mixed_Cached_OpenClip_Dataset_config (store_dict:dict, optimized:bool,
+                                       dataset_to_gpu:bool,
+                                       random_samples:int,
+                                       num_of_qubits:int, min_gates:int,
+                                       max_gates:int, gate_pool:list[str],
+                                       pad_constant:int, collate_fn:str,
+                                       bucket_batch_size:int,
+                                       num_down_scales:int)
+
+
+

source

+
+
+

Mixed_Cached_OpenClip_Dataset

+
+
 Mixed_Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),
+                                **parameters)
+
+

Dataset that uses multiple cached dataset and combines them with padding, either i) Bucket or ii) Max. Also provides a corresponding collate_fn for training.

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/dataset/qc_dataset.html b/dataset/qc_dataset.html new file mode 100644 index 0000000..564f3a2 --- /dev/null +++ b/dataset/qc_dataset.html @@ -0,0 +1,785 @@ + + + + + + + + + +genQC - Quantum circuit dataset + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Quantum circuit dataset

+
+ + + +
+ + + + +
+ + +
+ + +

Dataset for quantum circuits.

+
+

source

+
+

Qc_Config_Dataset_config

+
+
 Qc_Config_Dataset_config (store_dict:dict, optimized:bool,
+                           dataset_to_gpu:bool, random_samples:int,
+                           num_of_qubits:int, min_gates:int,
+                           max_gates:int, gate_pool:list[str])
+
+
+

source

+
+
+

Qc_Config_Dataset

+
+
 Qc_Config_Dataset (device:torch.device=device(type='cpu'), **parameters)
+
+

Dataset for quantum circuits, access gate_pool directly and all other paras with .params_config

+
+
init = {k:None for k in Qc_Config_Dataset.req_params}
+init["gate_pool"]  = ["qiskit.circuit.library.standard_gates.h.HGate",
+                      "qiskit.circuit.library.standard_gates.x.CXGate"]
+init["store_dict"] = {"x":"tensor", "y":"tensor_list"}
+
+a = Qc_Config_Dataset(**init)
+a.get_config()
+
+
{'target': '__main__.Qc_Config_Dataset',
+ 'device': 'cpu',
+ 'comment': '',
+ 'save_path': None,
+ 'save_datetime': '11/09/2023 18:48:57',
+ 'params': Qc_Config_Dataset_config(store_dict={'x': 'tensor', 'y': 'tensor_list'}, optimized=None, dataset_to_gpu=None, random_samples=None, num_of_qubits=None, min_gates=None, max_gates=None, gate_pool=['qiskit.circuit.library.standard_gates.h.HGate', 'qiskit.circuit.library.standard_gates.x.CXGate'])}
+
+
+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/examples/0_hello_circuit_files/figure-html/cell-17-output-1.png b/examples/0_hello_circuit_files/figure-html/cell-17-output-1.png new file mode 100644 index 0000000000000000000000000000000000000000..f955cfb0323edbee0e5a3bc04413e926150056c5 GIT binary patch literal 59382 zcmd?RWmuHm*FHQZC;|qZA|W}5bgM`VNOwzjcdH0Ucb7_c=MVzYokOQEba(#u-1qOk zpXdL4c|W|z`+RxlK*yO2uWMg>uf5i}&ULQKUq(t4^C8hg2n2!&6MHWQfuP-nKyKZj z-v{3)921U!H*R}j6?=IrLwhG3TLXxsj=i2TRRggOI9csl!fuRvAw;u z9S<|J#lOD*wX!v09?E~l2Tt<9T1?dr0>Rcn{kfGbm}LTiAoXDH-zqpKZO%BkD;P}P z?CpGT6>EAdmi>7k(Ox*pQ0VTX7~JExDmYINwSs##4#Kx&qed$V1%7Va>FUy(QpCe} z@%XVht+>3aKLyoe^aoTF2CtDP?JN%{B6&Pc>$HpNnv?8o`U?RuvXH9J$f1ilnt zEdP1kYF*#@=ilJHn#||TzaO7NWYO>b`|0-M{|^qGrhB7dcj=ZG?=7~@Vzn{{5wN_& z3ls)FdhH=cj$Eg`2mONDdvz*|c%;0JJyR67cAx!e@}1k!-@sMM7R-*;M{Ad3 z)JHNT@P$Q0=()L7t7cvEUbC_$PwqUE=KEn*SR?Y_!2@vl1XiOE`3tiS?)vrIymI}{ zr-zQ~{%e{7y z_wT!>h$agp()YgixZ4TD*XIEuJ5JO>gvr4JH 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ze{Rnd1h0uHrV0Vn1wI`H|9rnghwU9@o(My#SSf3cKA7IW|35!0meW1(`RT7Li62Uj)F2il;>T^T+fNWV0VQk?^ z?XMaYqoWV!t#3Jf+L?BuZ0mG;|K`*3#nTTHxD-EuG2H26|I>ll|CeIs|7U*ap%aqy X;O+h_uZSt)fmKn^kS~>e@ce%P(iN`W literal 0 HcmV?d00001 diff --git a/examples/editing_and_masking.html b/examples/editing_and_masking.html new file mode 100644 index 0000000..742c289 --- /dev/null +++ b/examples/editing_and_masking.html @@ -0,0 +1,1049 @@ + + + + + + + + + +genQC - Editing and masking of circuits + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + +
+ +
+
+

Editing and masking of circuits

+
+ + + +
+ + + + +
+ + +
+ + +

In this notebook we show editing and masking of circuits.

+
+
from genQC.imports import *
+from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
+from genQC.inference.infer_srv import convert_tensors_to_srvs, schmidt_rank_vector
+import genQC.platform.qcircuit_dataset_construction as data_const
+import genQC.util as util
+from qiskit.quantum_info import DensityMatrix
+
+
+
device = util.infer_torch_device()  # use cuda if we can
+util.MemoryCleaner.purge_mem()      # clean existing memory alloc
+
+
[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.
+
+
+
+

Setup and load

+

Load the pre-trained model

+
+
model_path = "../../saves/qc_unet_config_SRV_3to8_qubit/"
+pipeline   = DiffusionPipeline.from_config_file(model_path, device)
+
+
[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.
+
+
+

Set 20 sample steps and use rescaled guidance-formula.

+
+
pipeline.guidance_sample_mode = "rescaled"
+pipeline.scheduler.set_timesteps(40) 
+g = 7.5
+
+
+
+

1. Editing

+

Sample a random circuit with desired parameters as the circuit we want to edit:

+
+
srv_init       = [1, 1, 1, 2, 2]   # psi_0 state
+desired_length = 5                 # 5 gates initially placed
+
+
+
init_qc = data_const.get_specific_rnd_srv_circuit(srv_init, desired_length, pipeline.gate_pool)
+print("SRV is", schmidt_rank_vector(DensityMatrix(init_qc)))
+init_qc.draw("mpl")
+
+
SRV is [1, 1, 1, 2, 2]
+
+
+

+
+
+

The editing taks is analogous to image editing, we do img2img with conditioning and copy non-edit areas at every time step. Also called latent_filling.

+
+
def create_edited_circuits(pipeline, samples, qc, prompt, new_length, num_of_qubits, system_size, t_start_index):
+    #-------------------------------------------
+    # set mask - appending mask!
+    old_length = len(qc.data)
+
+    qubit_mask = torch.ones((system_size, new_length), device=device)
+    qubit_mask[:, :old_length] = 0
+    
+    #-------------------------------------------
+    # prepare and encode
+ 
+    gate_classes = data_const.gate_pool_to_gate_classes(pipeline.gate_pool)
+   
+    emb_org_image = data_const.encode_circuit(qc, system_size, gate_classes, new_length).unsqueeze(0).to(device)
+    emb_org_image = pipeline.model.embedd_clrs(emb_org_image)
+
+    emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))
+    
+    #-------------------------------------------
+    # prep condition
+    
+    c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))
+    c = c.repeat(samples, *[1]*(c.dim()-1))
+
+    #-------------------------------------------
+    # latent fill
+    out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)
+    out_tensor = pipeline.model.invert_clr(out_tensor)
+    out_tensor = out_tensor[:, :num_of_qubits]
+    out_tensor = torch.unique(out_tensor, dim=0) # we only are interested in unique circuits
+   
+    qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)
+
+    return qc_list, srv_list
+
+
+
samples    = 16   # how many circuits we sample
+new_length = 16   # how many gates the model can place 
+
+srv_target    = [2, 2, 2, 2, 2]  # desired target SRV
+
+num_of_qubits = len(srv_target)
+t_start_index = t_start_index = int(0.05 * pipeline.scheduler.timesteps.shape[0])  # time step index at which we start denoising
+
+prompt = f"Generate SRV: {srv_target}"  # model was trained with this phrase
+prompt
+
+
'Generate SRV: [2, 2, 2, 2, 2]'
+
+
+
+
# returns only distinct circuits
+edited_qc, srv_list = create_edited_circuits(pipeline, samples, init_qc, prompt, new_length, num_of_qubits, num_of_qubits, t_start_index)
+
+ +
+
+

Pick only correct ones:

+
+
correct_edited_qc = []
+for qc,srv in zip(edited_qc, srv_list):
+    if srv==srv_target: correct_edited_qc.append(qc)
+print(f"We found {len(correct_edited_qc)} correct distinct solutions.")
+
+
We found 12 correct distinct solutions.
+
+
+

Compare: initial circuit

+
+
init_qc.draw("mpl")
+
+

+
+
+

v.s. edited:

+
+
print("SRV is", schmidt_rank_vector(DensityMatrix(correct_edited_qc[0])))
+correct_edited_qc[0].draw("mpl", plot_barriers=False)
+
+
SRV is [2, 2, 2, 2, 2]
+
+
+

+
+
+
+
fig, axs = plt.subplots(2,4, figsize=(18,5), constrained_layout=True)
+for qc,ax in zip(correct_edited_qc, axs.flatten()): 
+    qc.draw("mpl", plot_barriers=False, ax=ax)
+plt.show()
+
+

+
+
+
+
+

2. Masking

+

First we set a desired mask, i.e. a specific layout of a quantum processor.

+
+
max_gates     = 16
+num_of_qubits = 5
+
+d = 3
+#------
+def con_set(q1, q2, x, d): 
+    qubit_mask[q1, x:x+d] = 1
+    qubit_mask[q2, x:x+d] = 1
+    return x+d
+
+#------
+x = 0
+
+qubit_mask = torch.zeros((num_of_qubits, max_gates), device=device) # mask: ones are getting filled, zeros are fixed !
+x = con_set(0, 1, x, d)
+x = con_set(1, 2, x, d)
+x = con_set(1, 3, x, d)
+x = con_set(3, 4, x, d)
+
+
+
def plot_mask():
+    fig = plt.figure(figsize=(3.7,2), constrained_layout=True)
+    plt.imshow(qubit_mask.cpu(), cmap="Greens")
+    plt.xticks(range(0, qubit_mask.shape[1], 2),fontsize=9)
+    plt.yticks(range(num_of_qubits), fontsize=9)
+    plt.xlabel("Gate sequence / time", fontsize=12)
+    plt.ylabel("Qubits", fontsize=12)
+    plt.show()
+plot_mask()
+
+

+
+
+
+
def get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask):
+    org_image = torch.zeros((1, system_size, max_gates), device=device, dtype=torch.int32) 
+    
+    padd_tok = len(pipeline.gate_pool) + 1
+    padd_pos = (torch.ceil(torch.tensor(target_num_gates) / 4) * 4).to(torch.int32)
+    org_image[:,                :, padd_pos:] = padd_tok
+    org_image[:, target_num_bits:,          ] = padd_tok
+
+    emb_org_image  = pipeline.model.embedd_clrs(org_image)
+    emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))
+    
+    return emb_org_images
+
+
+
def generate_pattern_SRV(pipeline, prompt, samples, system_size, num_of_qubits, max_gates, qubit_mask, t_start_index=0, target_num_gates=None, target_num_bits=None): 
+
+    if not exists(target_num_gates):
+        target_num_gates = max_gates
+
+    if not exists(target_num_bits):
+        target_num_bits = num_of_qubits
+    
+    emb_org_images = get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask)
+
+    #----------------
+    # prep condition
+
+    c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))
+    c = c.repeat(samples, *[1]*(c.dim()-1))
+
+    #----------------
+    # latent fill
+    
+    out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)
+    out_tensor = pipeline.model.invert_clr(out_tensor)
+    out_tensor = out_tensor[:, :num_of_qubits]
+    out_tensor = torch.unique(out_tensor, dim=0)
+     
+    qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)
+
+    return qc_list, srv_list
+
+

Now generate circuits corresponding to the mask.

+
+
samples    = 512              # how many circuits we sample
+srv_target = [2, 1, 2, 2, 2]  # desired target SRV
+
+assert len(srv_target)==qubit_mask.shape[0]
+
+prompt = f"Generate SRV: {srv_target}"  # model was trained with this phrase
+prompt
+
+
'Generate SRV: [2, 1, 2, 2, 2]'
+
+
+
+
qc_list, srv_list = generate_pattern_SRV(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, qubit_mask, t_start_index=1)
+
+ +
+
+

Pick only correct ones:

+
+
correct_qc = []
+for qc,srv in zip(qc_list, srv_list):
+    if srv==srv_target: correct_qc.append(qc)
+print(f"We found {len(correct_qc)} correct distinct solutions.")
+
+
We found 9 correct distinct solutions.
+
+
+

Let’s plot them. Mask:

+
+
plot_mask()
+
+

+
+
+

v.s. solution:

+
+
print("SRV is", schmidt_rank_vector(DensityMatrix(correct_qc[0])))
+correct_qc[0].draw("mpl", plot_barriers=False)
+
+
SRV is [2, 1, 2, 2, 2]
+
+
+

+
+
+
+
fig, axs = plt.subplots(1, min(len(correct_qc), 4), figsize=(18,5), constrained_layout=True)
+for qc,ax in zip(correct_qc, axs.flatten()): 
+    qc.draw("mpl", plot_barriers=False, ax=ax)
+plt.show()
+
+

+
+
+ + +
+ + Back to top
+ + +
+ + + + \ No newline at end of file diff --git a/examples/hello_circuit.html b/examples/hello_circuit.html new file mode 100644 index 0000000..81256d3 --- /dev/null +++ b/examples/hello_circuit.html @@ -0,0 +1,904 @@ + + + + + + + + + +genQC - Generate a circuit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Generate a circuit

+
+ + + +
+ + + + +
+ + +
+ + +

A minimal example to generate a circuit. We load a pre-trained (SRV, 3 to 8 qubit) model and condition on a given Schmidt-Rank-Vector (SRV).

+
+
from genQC.imports import *
+from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
+import genQC.inference.infer_srv as infer_srv
+import genQC.util as util
+
+
+
device = util.infer_torch_device()  # use cuda if we can
+util.MemoryCleaner.purge_mem()      # clean existing memory alloc
+
+
[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.
+
+
+
+
device
+
+
device(type='cuda')
+
+
+
+

Setup and load

+

Load the pre-trained model

+
+
model_path = "../../saves/qc_unet_config_SRV_3to8_qubit/"
+pipeline   = DiffusionPipeline.from_config_file(model_path, device)
+
+
[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.
+
+
+

Check on what gates the model was trained

+
+
pipeline.gate_pool
+
+
[qiskit.circuit.library.standard_gates.h.HGate,
+ qiskit.circuit.library.standard_gates.x.CXGate]
+
+
+

Set 20 sample steps and use rescaled guidance-formula.

+
+
pipeline.guidance_sample_mode = "rescaled"
+pipeline.scheduler.set_timesteps(20)
+
+
+
+

Inference / sampling

+

Set our desired condition SRV

+
+
srv           = [2, 1, 2, 1, 2]  # set your target SRV; can be 3 to 8 qubit
+num_of_qubits = len(srv)          
+
+prompt = f"Generate SRV: {srv}"  # model was trained with this phrase
+prompt
+
+
'Generate SRV: [2, 1, 2, 1, 2]'
+
+
+

Define sample parameters

+
+
g         = 7.5     # guidance scale
+max_gates = 16      # how many time steps the tensor encoding has
+samples   = 32      # how many circuits to generate
+
+

Sample tokenized circuits

+
+
out_tensor = infer_srv.generate_srv_tensors(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, g, no_bar=False)
+
+ +
+
+
[INFO]: (generate_srv_tensors) Generated 32 tensors
+
+
+

Check how many distinct tensors we got:

+
+
out_tensor.unique(dim=0).shape[0]
+
+
32
+
+
+

Let’s look what is generated. Note, 3 is the padding token (or empty action).

+
+
out_tensor[:2]
+
+
tensor([[[ 0,  2,  1,  2, -2,  0,  2,  0,  0,  2,  3,  3,  3,  3,  3,  3],
+         [-2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3],
+         [ 2, -2,  0, -2,  2,  1,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3],
+         [ 0,  0,  0,  0,  0,  0, -2,  1,  0,  0,  3,  3,  3,  3,  3,  3],
+         [ 0,  0,  0,  0,  0,  0,  0,  0,  1, -2,  3,  3,  3,  3,  3,  3]],
+
+        [[ 1, -2,  0,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
+         [ 0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
+         [ 0,  2,  0, -2,  1,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
+         [ 0,  0, -2,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
+         [ 0,  0,  2,  2,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3]]])
+
+
+
+
+

Convert to quiskit circuit

+

To get a quiskit circuit we need to do:

+
    +
  • apply cos. sim. to go from embeddings to token matrices (the function infer_srv.generate_srv_tensors did this already)
    • +
  • parse token matrix to quiskit and filter out error circuits
    • +
  • calculate SRV and plot circuits
    • +
+
+
qc_list, error_cnt, svr_list = infer_srv.convert_tensors_to_srvs(out_tensor, pipeline.gate_pool)
+
+

Generated error circuits:

+
+
error_cnt
+
+
0
+
+
+

What SRVs did we get:

+
+
svr_list[:4]
+
+
[[2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2]]
+
+
+

That is an accuracy of:

+
+
sum(srv==x for x in svr_list)/len(svr_list)
+
+
0.9375
+
+
+

Finally plot some of the circuits:

+
+
fig, axs = plt.subplots(2, 4, figsize=(18,5), constrained_layout=True)
+for qc,is_svr,ax in zip(qc_list, svr_list, axs.flatten()): 
+    qc.draw("mpl", plot_barriers=False, ax=ax)
+    ax.set_title(f"{'Correct' if is_svr==srv else 'NOT correct'}, is SRV = {is_svr}")
+plt.show()
+
+

+
+
+ + +
+ + Back to top
+ + +
+ + + + \ No newline at end of file diff --git a/examples/unitary_compilation.html b/examples/unitary_compilation.html new file mode 100644 index 0000000..9b9343d --- /dev/null +++ b/examples/unitary_compilation.html @@ -0,0 +1,978 @@ + + + + + + + + + +genQC - Compile unitaries + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Compile unitaries

+
+ + + +
+ + + + +
+ + +
+ + +

In this notebook we want use the unitary compilation model.

+
+
from genQC.imports import *
+from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
+from qiskit import QuantumCircuit
+from genQC.inference.infer_compilation import generate_comp_tensors, get_gate_and_U_acc
+from genQC.printing import display_colums
+import genQC.platform.qcircuit_dataset_construction as data_const
+import qiskit.quantum_info as qi
+import genQC.util as util
+import ast
+
+
+
device = util.infer_torch_device()  # use cuda if we can
+util.MemoryCleaner.purge_mem()      # clean existing memory alloc
+
+
[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.
+
+
+
+
def str_cond_to_gate_indices(y):   # helper function, used to check if only allowed gates were used by the model!
+    assert y[:15] == "Compile using: "
+    c            = ast.literal_eval(y[15:]) 
+    gate_classes = data_const.gate_pool_to_gate_classes(pipeline.gate_pool)
+    gate_clrs    = [0] + [gate_classes[ic] for ic in c]  # 0 is empty, always allowed!
+    return gate_clrs
+
+
+

Setup and load

+

Load the pre-trained model

+
+
model_path = "../../saves/qc_unet_config_Compilation_3_qubit/"
+pipeline   = DiffusionPipeline.from_config_file(model_path, device)
+
+
[INFO]: `genQC.models.unet_qc.QC_Compilation_UNet` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.
+
+
+

Set 20 sample steps and use rescaled guidance-formula.

+
+
pipeline.guidance_sample_mode = "rescaled"
+pipeline.scheduler.set_timesteps(20) 
+g = 7.5
+
+

The model was trained with a gate pool of:

+
+
pipeline.gate_pool
+
+
[qiskit.circuit.library.standard_gates.h.HGate,
+ qiskit.circuit.library.standard_gates.x.CXGate,
+ qiskit.circuit.library.standard_gates.z.ZGate,
+ qiskit.circuit.library.standard_gates.x.XGate,
+ qiskit.circuit.library.standard_gates.x.CCXGate,
+ qiskit.circuit.library.standard_gates.swap.SwapGate]
+
+
+
+
+

Compile a unitary

+

Compile a given unitary \(U\). Note, there has to be a solution with the pipeline.gate_pool in order to find the exact solution.

+
+
def compile_and_plot(U, prompt):
+    U_r, U_i = torch.Tensor(np.real(U)), torch.Tensor(np.imag(U))
+    U_tensor = torch.stack([U_r, U_i], dim=0)
+    
+    out_tensor                           = generate_comp_tensors(pipeline, prompt, U_tensor, samples, num_of_qubits, num_of_qubits, max_gates, g, unique=True)
+    _, _, _, _, _, comb_corr_qc, _, _, _ = get_gate_and_U_acc(out_tensor, str_cond_to_gate_indices(prompt), U_tensor, pipeline.gate_pool, num_of_qubits, max_gates)
+    comb_corr_qc                         = sorted(comb_corr_qc, key=lambda x: len(x.data)) #sort to get the shortest solutions
+
+    fig, axs = plt.subplots(1,4, figsize=(18,5), constrained_layout=True)
+    axs[0].set_title(f"{prompt}")
+    for qc,ax in zip(comb_corr_qc, axs.flatten()): 
+        qc.draw("mpl", plot_barriers=False, ax=ax)
+    plt.show()
+
+
+
samples       = 512
+num_of_qubits = 3
+max_gates     = 12
+
+
+
prompt = "Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']" # model was trained with phrases like this, allow full gate set
+prompt
+
+
"Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']"
+
+
+
+

Exercise 1

+

Inspired from (quantumcomputing.stackexchange.com/questions/13821/generate-a-3-qubit-swap-unitary-in-terms-of-elementary-gates/13826). Note, this unitary WAS in the training set.

+
+
U = np.matrix([[1,0,0,0,0,0,0,0],
+               [0,1,0,0,0,0,0,0],
+               [0,0,1,0,0,0,0,0],
+               [0,0,0,0,1,0,0,0],
+               [0,0,0,1,0,0,0,0],
+               [0,0,0,0,0,1,0,0],
+               [0,0,0,0,0,0,1,0],
+               [0,0,0,0,0,0,0,1]], dtype=np.complex128) 
+
+assert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary
+
+

Plot correct (exact) compiled circuits:

+
+
compile_and_plot(U, prompt)
+
+

+
+
+
+
+

Exercise 2

+

Inspired from (quantumcomputing.stackexchange.com/questions/12439/procedures-and-intuition-for-designing-simple-quantum-circuits/12440). Note, this unitary WAS in the training set.

+
+
U = np.matrix([[1,0,0,0,0,0,0,0],
+               [0,0,0,0,0,0,0,1],
+               [0,1,0,0,0,0,0,0],
+               [0,0,1,0,0,0,0,0],
+               [0,0,0,1,0,0,0,0],
+               [0,0,0,0,1,0,0,0],
+               [0,0,0,0,0,1,0,0],
+               [0,0,0,0,0,0,1,0]], dtype=np.complex128) 
+
+assert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary
+
+

Plot correct (exact) compiled circuits:

+
+
compile_and_plot(U, prompt)
+
+

+
+
+
+
+

Exercise 3

+

A randomly generated unitary (from a random circuit). This unitary WAS NOT in the training set, it is new to the model!

+
+
U = np.matrix([[ 0.70710678,  0.        ,  0.        , 0.        ,  0.70710678,  0.        , 0.        ,  0.        ],
+               [ 0.        , -0.70710678,  0.        , 0.        ,  0.        , -0.70710678, 0.        ,  0.        ],
+               [-0.70710678,  0.        ,  0.        , 0.        ,  0.70710678,  0.        , 0.        ,  0.        ],
+               [ 0.        ,  0.70710678,  0.        , 0.        ,  0.        , -0.70710678, 0.        ,  0.        ],
+               [ 0.        ,  0.        ,  0.70710678, 0.        ,  0.        ,  0.        , 0.        ,  0.70710678],
+               [ 0.        ,  0.        ,  0.        , 0.70710678,  0.        ,  0.        , 0.70710678,  0.        ],
+               [ 0.        ,  0.        , -0.70710678, 0.        ,  0.        ,  0.        , 0.        ,  0.70710678],
+               [ 0.        ,  0.        ,  0.        ,-0.70710678,  0.        ,  0.        , 0.70710678,  0.        ]], dtype=np.complex128)
+
+assert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary
+
+

Plot correct (exact) compiled circuits:

+
+
compile_and_plot(U, prompt)
+
+

+
+
+
+
+
+

Transpile and discover

+

Set an initial circuit we want to transpile, optimize or use for discovering sub-arrangements:

+
+
qc = QuantumCircuit(3)
+qc.h(2)
+qc.cx(0,1)
+qc.cx(2,1)
+qc.h(1)
+qc.x(1)
+qc.h(1)
+qc.x(2)
+
+U = qi.Operator(qc).to_matrix() # the unitary of the circuit
+
+#-----------------------------------------
+
+fig = qc.draw("mpl")
+fig
+
+

+
+
+

We set different gate pool targets to see what the model gives us:

+
+
cs_1 = f"Compile using: {[x().name for x in pipeline.gate_pool]}", "all"
+
+cs_2 = "Compile using: ['h', 'cx', 'z', 'ccx']" , "no x, no swap"    
+cs_3 = "Compile using: ['h', 'cx', 'x', 'ccx']" , "no z, no swap"    
+cs_4 = "Compile using: ['h', 'x', 'ccx']"       , "no cx, no z, no swap" 
+cs_5 = "Compile using: ['h', 'z', 'x', 'ccx']"  , "no cx, no swap"  
+
+cs = [cs_1, cs_2, cs_3, cs_4, cs_5]
+cs
+
+
[("Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']", 'all'),
+ ("Compile using: ['h', 'cx', 'z', 'ccx']", 'no x, no swap'),
+ ("Compile using: ['h', 'cx', 'x', 'ccx']", 'no z, no swap'),
+ ("Compile using: ['h', 'x', 'ccx']", 'no cx, no z, no swap'),
+ ("Compile using: ['h', 'z', 'x', 'ccx']", 'no cx, no swap')]
+
+
+
+
samples       = 512
+num_of_qubits = 3
+max_gates     = 12
+
+

Compile with the different gate-sets and plot correct (exact) compiled circuits. Note, some of the circuits might look the same but the gate time-sequences are distinct. Qiskit reorders “parallel” gates to make smaller plots.

+
+
for c, note in cs: compile_and_plot(U, c)
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..a419a19 --- /dev/null +++ b/index.html @@ -0,0 +1,854 @@ + + + + + + + + + + +genQC - genQC · Generative Quantum Circuits + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

genQC · Generative Quantum Circuits

+
+ +
+
+ Generating quantum circuits with diffusion models +
+
+ + +
+ + + + +
+ + +
+ + +

+awesome generative-models diffusion-models python-3.10 +

+

Code repository for generating quantum circuits with diffuison models. [Paper]

+

+
+

The codebase

+

All weights and functions are contained within this repo. For the CLIP model weights we use the OpenCLIP library, which will download (and cache) the CLIP model on first usage of our pipeline. In case you prefer reading a documentation rather than notebooks or code see florianfuerrutter.github.io/genQC/.

+

The repo inlcudes:

+
    +
  1. saves/ the configs and weights of the pre-trained models.
    2. +
  2. genQC/ a full release of our used diffusion pipeline.
    3. +
  3. src/examples examples how to reproduce some figures of the Paper.
    4. +
  4. src/ the source notebooks for nbdev.
    5. +
+
+
+

Examples

+
+

Minimal example

+

A minimal example to generate a 5 qubit circuit conditioned on a SRV of \([1,1,1,2,2]\):

+
+
from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
+from genQC.inference.infer_srv import generate_srv_tensors, convert_tensors_to_srvs
+
+model_path = "../saves/qc_unet_config_SRV_3to8_qubit/"
+pipeline   = DiffusionPipeline.from_config_file(model_path, "cpu")  
+pipeline.scheduler.set_timesteps(20) 
+
+out_tensor           = generate_srv_tensors(pipeline, "Generate SRV: [1,1,1,2,2]", samples=1, system_size=5, num_of_qubits=5, max_gates=16, g=7.5) 
+qc_list, _, svr_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool)
+
+
[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cpu.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cpu.
+[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.
+
+
+
+
print(f"is SRV {svr_list[0]}")
+qc_list[0].draw("mpl")
+
+
is SRV [1, 1, 1, 2, 2]
+
+
+

+
+
+
+
+

Included examples

+

Examples are provided in the directory src/examples/.

+ +
+
+
+

Installation:

+
+

1. Clone

+
git clone https://github.com/FlorianFuerrutter/genQC.git
+cd genQC
+
+
+

2. Install genQC

+

This library is build using jupyter notebooks and nbdev. To install the library use in the clone directory:

+
pip install -e .
+

Note, this will install missing requirements automatically: [torch numpy matplotlib scipy pandas omegaconf qiskit tqdm joblib open_clip_torch ipywidgets pylatexenc]. You may want to install some of them manually beforehand, e.g. pytorch for specific cuda support pytorch.org/get-started/locally.

+
+
+

3. Run example

+

You can run the provided 0_hello_circuit example to test your installation.

+
+
+
+

License

+

The code and weights in this repository are released under the MIT License.

+
+
+

BibTeX

+

We kindly ask you to cite our paper if any of the previous material was useful for your work.

+
@article{fuerrutter2023quantum,
+      title={Quantum circuit synthesis with diffusion models}, 
+      author={Florian Fürrutter and Gorka Muñoz-Gil and Hans J. Briegel},
+      year={2023},
+      eprint={2311.02041},
+      publisher = {arXiv},
+      archivePrefix={arXiv},
+      primaryClass={quant-ph},
+      url = {https://arxiv.org/abs/2311.02041},
+      doi = {/10.48550/arXiv.2311.02041},
+}
+ + +
+ + Back to top
+ +
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+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inference compilation functions

+
+ + + +
+ + + + +
+ + +
+ + +
+

Misc

+
+

source

+
+

split_U_to_tensor

+
+
 split_U_to_tensor (U:numpy.ndarray)
+
+
+

source

+
+
+

get_new_unitary_indices

+
+
 get_new_unitary_indices (Us, dataset, silent=False)
+
+
+

source

+
+
+

get_new_unitary_indices_batch

+
+
 get_new_unitary_indices_batch (Us, dataset, auto_batch_size=32,
+                                silent=False, n_jobs=1)
+
+
+
+
+

Generation

+
+

source

+
+

generate_comp_tensors

+
+
 generate_comp_tensors (pipeline, prompt, U, samples, system_size,
+                        num_of_qubits, max_gates, g, no_bar=True,
+                        unique=False, auto_batch_size=512)
+
+
+
+
+

Accuracy

+
+

source

+
+

check_correct_gates

+
+
 check_correct_gates (qc, num_of_qubits, gate_pool, max_gates,
+                      allowed_gate_clrs)
+
+
+

source

+
+
+

check_correct_unitary_exact

+
+
 check_correct_unitary_exact (qc, U)
+
+
+

source

+
+
+

check_correct_unitary_distance

+
+
 check_correct_unitary_distance (qc, target_U, norms)
+
+
+

source

+
+
+

get_gate_and_U_acc

+
+
 get_gate_and_U_acc (out_tensor, allowed_gate_clrs, U, gate_pool,
+                     num_of_qubits, max_gates, norms=[], no_bar=True)
+
+
+
+
+

Tests

+
+

source

+
+

test_comp_acc

+
+
 test_comp_acc (pipeline, samples, system_size, gate_pool, num_of_qubits,
+                max_gates, g, str_cond_to_gate_indices:<built-
+                infunctioncallable>, Us, ys, train_dataset=None, norms=[])
+
+
+

source

+
+
+

test_comp_acc_on_testset

+
+
 test_comp_acc_on_testset (pipeline, samples, num_of_U, system_size,
+                           gate_pool, num_of_qubits, max_gates, g,
+                           str_cond_to_gate_indices:<built-
+                           infunctioncallable>, prompt_mod:<built-
+                           infunctioncallable>, test_dataset,
+                           train_dataset=None, norms=[], fix_y=None)
+
+

returns: acc_s, gate_acc_s, u_acc_s, uniques_cnt_s, error_cnt_s, num_found_circuits_s, task_qc_len_s

+
+

source

+
+
+

test_comp_acc_on_rnd_samples

+
+
 test_comp_acc_on_rnd_samples (pipeline, samples, num_of_U, system_size,
+                               gate_pool, num_of_qubits, max_gates, g,
+                               str_cond_to_gate_indices:<built-
+                               infunctioncallable>, prompt_mod:<built-
+                               infunctioncallable>, rnd_min_gates,
+                               rnd_max_gates, train_dataset=None,
+                               norms=[], fix_y=None)
+
+

returns: acc_s, gate_acc_s, u_acc_s, uniques_cnt_s, error_cnt_s, num_found_circuits_s, task_qc_len_s

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
pipeline
samples
num_of_U
system_size
gate_pool
num_of_qubits
max_gates
g
str_cond_to_gate_indicescallable
prompt_modcallabletakes a single prompt and returns it modified
rnd_min_gates
rnd_max_gates
train_datasetNoneTypeNone
normslist[]
fix_yNoneTypeNone
+
+
+
+

Plot

+
+

source

+
+

plot_hist_overview

+
+
 plot_hist_overview (out_tuple, num_of_samples_per_U, rnd_min_gates,
+                     rnd_max_gates, max_gates, num_of_qubits)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/inference/infer_gate_hist.html b/inference/infer_gate_hist.html new file mode 100644 index 0000000..6ac9b58 --- /dev/null +++ b/inference/infer_gate_hist.html @@ -0,0 +1,736 @@ + + + + + + + + + +genQC - Inference gate distribution + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inference gate distribution

+
+ + + +
+ + + + +
+ + +
+ + +
+

Gate length

+
+

source

+
+

get_tensor_gate_length

+
+
 get_tensor_gate_length (clr_tensor, padding_token=0)
+
+

Careful with padding tokens!

+
+

source

+
+
+

get_circuit_gate_length

+
+
 get_circuit_gate_length (qcs)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/inference/infer_misc.html b/inference/infer_misc.html new file mode 100644 index 0000000..1bac476 --- /dev/null +++ b/inference/infer_misc.html @@ -0,0 +1,741 @@ + + + + + + + + + +genQC - Inference miscellaneous functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inference miscellaneous functions

+
+ + + +
+ + + + +
+ + +
+ + +
+

Misc

+
+

source

+
+

get_rnd_gatepool_subset

+
+
 get_rnd_gatepool_subset (gate_pool, min_sub_gate_pool_cnt=2)
+
+
+
+
+

Convertion

+
+

source

+
+

convert_tensors_to_circuits

+
+
 convert_tensors_to_circuits (out_tensor, gate_pool, place_barrier=False)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/inference/infer_srv.html b/inference/infer_srv.html new file mode 100644 index 0000000..498b90e --- /dev/null +++ b/inference/infer_srv.html @@ -0,0 +1,922 @@ + + + + + + + + + +genQC - Inference SRV functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inference SRV functions

+
+ + + +
+ + + + +
+ + +
+ + +
+

Generation

+
+

source

+
+

get_all_srvs

+
+
 get_all_srvs (num_of_qubits)
+
+
+

source

+
+
+

generate_srv_tensors

+
+
 generate_srv_tensors (pipeline, prompt, samples, system_size,
+                       num_of_qubits, max_gates, g, no_bar=True,
+                       unique=False, auto_batch_size=512)
+
+
+
+
+

Convertion

+
+

source

+
+

convert_tensors_to_srvs

+
+
 convert_tensors_to_srvs (out_tensor, gate_pool, sort_srv=False,
+                          place_barrier=False, n_jobs=1)
+
+
+
+
+

Accuracy

+
+

source

+
+

get_srv_accuracy

+
+
 get_srv_accuracy (svr_list, target_srv)
+
+
+
+
+

Tests

+
+

source

+
+

true_sample_bin_dist

+
+
 true_sample_bin_dist (samples_per_bin, bin_size)
+
+
+

source

+
+
+

test_srv_clr_distribution_bin_samples

+
+
 test_srv_clr_distribution_bin_samples (pipeline, samples_per_bin,
+                                        system_size, num_of_qubits,
+                                        max_gates, g, gate_pool,
+                                        silent=False, device='cpu',
+                                        U=None, prompt_mod:<built-
+                                        infunctioncallable>=<function
+                                        <lambda>>, only_diag=False,
+                                        n_jobs=1)
+
+
+

source

+
+
+

test_srv_clr_distribution

+
+
 test_srv_clr_distribution (pipeline, samples_per_srv, system_size,
+                            num_of_qubits, max_gates, g, gate_pool,
+                            silent=False, device='cpu', U=None,
+                            prompt_mod:<built-
+                            infunctioncallable>=<function <lambda>>,
+                            dist_srvs=None, cond_srvs=None,
+                            only_diag=False, n_jobs=1)
+
+
+

source

+
+
+

test_guidance_dep

+
+
 test_guidance_dep (pipeline, srvs, samples, system_size, num_of_qubits,
+                    max_gates, gs, gate_pool, prompt_mod:<built-
+                    infunctioncallable>=<function <lambda>>, U=None,
+                    n_jobs=1)
+
+
+

source

+
+
+

test_srv_acc_vs_length

+
+
 test_srv_acc_vs_length (pipeline, samples, system_size, num_of_qubits,
+                         max_gates, g, gate_pool, prompt_mod:<built-
+                         infunctioncallable>=<function <lambda>>, U=None,
+                         n_jobs=1)
+
+
+

source

+
+
+

test_srv_acc_vs_maxLength

+
+
 test_srv_acc_vs_maxLength (pipeline, samples_per_bin, system_size,
+                            num_of_qubits, max_gates_list, g, gate_pool,
+                            prompt_mod:<built-
+                            infunctioncallable>=<function <lambda>>,
+                            U=None, n_jobs=1)
+
+
+

source

+
+
+

test_srv_length_distribution

+
+
 test_srv_length_distribution (pipeline, samples_per_bin, system_size,
+                               num_of_qubits, max_gates, g, gate_pool,
+                               silent=False, U=None, prompt_mod:<built-
+                               infunctioncallable>=<function <lambda>>,
+                               n_jobs=1)
+
+
+
+
+

Plot

+
+

source

+
+

plot_srv_clr_distribution_hist

+
+
 plot_srv_clr_distribution_hist (values, samples, num_of_qubits,
+                                 save=False, dist_srvs=None,
+                                 cond_srvs=None)
+
+
+

source

+
+
+

plot_srv_clr_distribution_bin_accuracy

+
+
 plot_srv_clr_distribution_bin_accuracy (values, samples, num_of_qubits,
+                                         save=False,
+                                         plot_percentages=False,
+                                         trainSet_srv=None)
+
+
+

source

+
+
+

plot_guidance_dep

+
+
 plot_guidance_dep (srvs, gs, guidance_dep_out, samples, save=False)
+
+
+

source

+
+
+

plot_srv_acc_vs_length

+
+
 plot_srv_acc_vs_length (ent_ls, ent_accs, ent_cnts, ent_labels, samples,
+                         plot_dist=True, save=False)
+
+
+

source

+
+
+

plot_srv_acc_vs_maxLength

+
+
 plot_srv_acc_vs_maxLength (ent_accs, ent_labels, max_gates_list, samples,
+                            plot_dist=True, save=False)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/metrics.html b/metrics.html new file mode 100644 index 0000000..bd14b69 --- /dev/null +++ b/metrics.html @@ -0,0 +1,796 @@ + + + + + + + + + +genQC - Metrics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Metrics

+
+ + + +
+ + + + +
+ + +
+ + +

Definition of metrics used during training.

+
+

source

+
+

Metric

+
+
 Metric (name:str, device)
+
+

Base metric class.

+
+

source

+
+
+

Mean

+
+
 Mean (name:str, device)
+
+

Mean metric, used for loss ..

+
+

source

+
+
+

Accuracy

+
+
 Accuracy (name:str, device)
+
+

Accuracy metric.

+

Example usage:

+
+
a = Accuracy("mean", "cpu")
+print(a, a.empty)
+
+a.update_state(torch.Tensor([3,2,2,1]), torch.Tensor([1,2,2,1]))
+print(a, a.empty)
+
+a.update_state(torch.Tensor([1,2,2,3]), torch.Tensor([1,2,2,3]))
+print(a, a.empty)
+
+a.reset_state()
+print(a, a.empty)
+
+
mean=nan True
+mean=0.75 False
+mean=0.875 False
+mean=nan True
+
+
+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/models/config_model.html b/models/config_model.html new file mode 100644 index 0000000..245a2b8 --- /dev/null +++ b/models/config_model.html @@ -0,0 +1,728 @@ + + + + + + + + + +genQC - Config model + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Config model

+
+ + + +
+ + + + +
+ + +
+ + +

Model base class that handles loading and storing from/to config-files.

+
+

Model

+
+

source

+
+

Config_Model

+
+
 Config_Model ()
+
+

A basic nn.Module with IO functionality.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/models/frozen_open_clip.html b/models/frozen_open_clip.html new file mode 100644 index 0000000..f4b9606 --- /dev/null +++ b/models/frozen_open_clip.html @@ -0,0 +1,875 @@ + + + + + + + + + +genQC - Frozen OpenCLIP + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Frozen OpenCLIP

+
+ + + +
+ + + + +
+ + +
+ + +
+

CLIP model

+
+

source

+
+

FrozenOpenCLIPEmbedder_config

+
+
 FrozenOpenCLIPEmbedder_config (arch:str, version:str, device:str,
+                                max_length:int, freeze:bool, layer:str)
+
+
+

source

+
+
+

FrozenOpenCLIPEmbedder

+
+
 FrozenOpenCLIPEmbedder (arch='ViT-H-14', version='laion2b_s32b_b79k',
+                         device='cpu', max_length=77, freeze=True,
+                         layer='penultimate')
+
+

Loads and freezes the OpenCLIP transformer encoder for text prompts.

+
+
a = FrozenOpenCLIPEmbedder()
+
+
+
p="[1, 2, 2]"
+a.tokenize_and_push_to_device(p)
+
+
tensor([[49406,   314,   272,   267,   273,   267,   273,   316, 49407,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0]])
+
+
+
+
a.tokenize_and_push_to_device("").shape
+
+
torch.Size([1, 77])
+
+
+
+
c = a.tokenize_and_push_to_device(["1,1,2", "2,2,2"])
+enc = a(c)
+enc.shape, enc
+
+
(torch.Size([2, 77, 1024]),
+ tensor([[[-0.3134, -0.4476, -0.0082,  ...,  0.2542, -0.0324, -0.2960],
+          [ 0.0668, -1.2381,  0.9908,  ...,  0.1785,  0.1592, -0.4320],
+          [ 0.6988, -0.2168, -1.2912,  ...,  2.1063, -0.0302, -0.5666],
+          ...,
+          [ 0.4703, -1.4072, -0.4847,  ..., -0.1257, -0.1650,  0.1206],
+          [ 0.5117, -1.3949, -0.4672,  ..., -0.4288, -0.2166,  0.2904],
+          [ 0.1480, -2.1998, -1.1187,  ...,  0.0823, -0.4157,  0.6236]],
+ 
+         [[-0.3134, -0.4476, -0.0082,  ...,  0.2542, -0.0324, -0.2960],
+          [-0.1180, -1.6322,  1.2987,  ..., -0.1378, -0.1529, -0.3377],
+          [-0.7251, -0.8167, -0.9966,  ...,  2.2262, -0.2325, -0.0138],
+          ...,
+          [ 0.3887, -1.3395, -0.5868,  ..., -0.1621, -0.0594,  0.1253],
+          [ 0.4360, -1.3350, -0.5684,  ..., -0.4643, -0.1131,  0.2847],
+          [ 0.1691, -2.1725, -1.1441,  ...,  0.0633, -0.3175,  0.7041]]]))
+
+
+
+
open_clip.decode(c[1])
+
+
'<start_of_text>2 , 2 , 2 <end_of_text>!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'
+
+
+
+
+
+

Cached model

+

Model takes now also (batched) scalar int values that are defined to unique conditions like \([1,2,2]=4\). If input is now such int the output is the cached pre-embedded tensor. If a non int, like a token string is passed we just do the normal embedding live.

+
+

source

+
+

CachedFrozenOpenCLIPEmbedder

+
+
 CachedFrozenOpenCLIPEmbedder (arch='ViT-H-14',
+                               version='laion2b_s32b_b79k', device='cpu',
+                               max_length=77, freeze=True,
+                               layer='penultimate')
+
+

Adds caching support to FrozenOpenCLIPEmbedder.

+
+
a = CachedFrozenOpenCLIPEmbedder()
+p = ["1,1,2", "2,2,2"]
+
+a.generate_cache(p)
+
+ +
+
+
[INFO]: caching trying to allocate memory (2, 77, 1024) on cpu approx. 0.001 GB
+
+
+
+
c_cached   = torch.tensor([0,0,1], device=a.device)
+c_uncached = a.tokenize_and_push_to_device(["1,1,2", "1,1,2", "2,2,2"])
+
+enc_cached   = a(c_cached)
+enc_uncached = a(c_uncached)
+
+enc_cached.shape, enc_uncached.shape, torch.allclose(enc_cached, enc_uncached, atol=1e-5)
+
+
(torch.Size([3, 77, 1024]), torch.Size([3, 77, 1024]), True)
+
+
+ + +
+
+ + Back to top
+ + +
+ + + + \ No newline at end of file diff --git a/models/layers.html b/models/layers.html new file mode 100644 index 0000000..c29cdf1 --- /dev/null +++ b/models/layers.html @@ -0,0 +1,880 @@ + + + + + + + + + +genQC - Layers + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Layers

+
+ + + +
+ + + + +
+ + +
+ + +

Common model layers.

+
+

Basic scaling blocks

+
+

source

+
+

DownBlock2D

+
+
 DownBlock2D (in_ch, out_ch, kernel_size=2, stride=2, padding=0,
+              use_conv=True)
+
+

A 2d down scale block.

+
+

source

+
+
+

UpBlock2D

+
+
 UpBlock2D (in_ch, out_ch, kernel_size=2, stride=2, padding=0,
+            use_conv=True)
+
+

A 2d up scale block.

+
+
+
+

ResNet blocks

+
+

source

+
+

ResBlock2D

+
+
 ResBlock2D (in_ch, out_ch, kernel_size, skip=True)
+
+

A 2d residual block.

+
+

source

+
+
+

ResBlock2D_Conditional

+
+
 ResBlock2D_Conditional (in_ch, out_ch, t_emb_size, kernel_size,
+                         skip=True)
+
+

A 2d residual block with input of a time-step \(t\) embedding.

+
+
+
+

FeedForward layer

+
+

source

+
+

FeedForward

+
+
 FeedForward (in_ch, out_ch, inner_mult=1)
+
+

A small dense feed-forward network as used in transformers.

+
+
+
+

Position embedding layers

+

Create sinusoidal position embeddings, same as those from the transformer:

+

+
+

source

+
+

PositionalEncoding

+
+
 PositionalEncoding (d_model:int, dropout:float=0.0, max_len:int=5000)
+
+

An absolute pos encoding layer.

+
+

source

+
+
+

TimeEmbedding

+
+
 TimeEmbedding (d_model:int, dropout:float=0.0, max_len:int=5000)
+
+

A time embedding layer

+
+

source

+
+
+

PositionalEncodingTransposed

+
+
 PositionalEncodingTransposed (d_model:int, dropout:float=0.0,
+                               max_len:int=5000)
+
+

An absolute pos encoding layer.

+
+

source

+
+
+

PositionalEncoding2D

+
+
 PositionalEncoding2D (d_model:int, dropout:float=0.0, max_len:int=5000)
+
+

A 2D absolute pos encoding layer.

+
+
a = torch.zeros((1, 4, 3, 4))
+l = PositionalEncoding2D(d_model=4)   
+
+l(a)[0].shape
+
+
torch.Size([4, 3, 4])
+
+
+ + +
+
+ + Back to top
+ +
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zn_Gk2O8OgXjU;d#H6TH{D+Fd5e1@t77RpL)?+MoIE-jvUCKN*i1}DTo`kt=#Qv2fg z>1;r{xw7QFKOoH&GIz~G@m+jD`ek3%24{ZxW8O#}<>^lw&X~>`yz{MI8PX^I&tEJE zq@|UUn{MgF<#(0rIBl!gesQD3q96p|v_5`>XV=4EWv2CxN+BwllG4)RuC6+#u^q=C zZ}QzZL^={_;~@|v5SO`_q%n~SH{agAtK6S`P)AGH_g&>Sg;L`>{0Ao{EFK6{)@G>H zflW{UQB%D~-PoR + + + + + + + + +genQC - Transformers + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Transformers

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+ + + +
+ + + + +
+ + +
+ + +
+

Attention blocks

+
+

source

+
+

BasisSelfAttnBlock

+
+
 BasisSelfAttnBlock (ch, num_heads, dropout=0)
+
+

A self attention block, i.e. a transformer encoder.

+
+

source

+
+
+

BasisCrossAttnBlock

+
+
 BasisCrossAttnBlock (ch, cond_emb_size, num_heads, dropout=0.0)
+
+

A cross attention block, i.e. a transformer decoder.

+
+
+
+

Spatial residual transformers

+
+

source

+
+

SpatialTransformerSelfAttn

+
+
 SpatialTransformerSelfAttn (ch, num_heads, depth, dropout=0.0)
+
+

A spatial residual transformer, only uses self-attention.

+
+

source

+
+
+

SpatialTransformer

+
+
 SpatialTransformer (ch, cond_emb_size, num_heads, depth, dropout=0.0)
+
+

A spatial residual transformer, uses self- and cross-attention on conditional input.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/models/unet_qc.html b/models/unet_qc.html new file mode 100644 index 0000000..8df71dc --- /dev/null +++ b/models/unet_qc.html @@ -0,0 +1,815 @@ + + + + + + + + + +genQC - Conditional qc-UNet + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Conditional qc-UNet

+
+ + + +
+ + + + +
+ + +
+ + +

Quantum circuit U-Net architecture predicting the noise for noisy quantum circuits.

+
+

Blocks

+
+

source

+
+

UNet_block

+
+
 UNet_block (ch_in, ch_out, t_emb_size, cond_emb_size, num_heads=8,
+             num_res_blocks=1, transformer_depth=1)
+
+

The basic block of the U-Net. Is conditioned via cross-attention in SpatialTransformer and addition of the time ebedding in ResBlock2D_Conditional.

+
+

source

+
+
+

Encoder

+
+
 Encoder (model_features, t_emb_size, cond_emb_size, num_heads,
+          num_res_blocks, transformer_depths)
+
+

Encoder definition of the U-Net.

+
+

source

+
+
+

Decoder

+
+
 Decoder (model_features, t_emb_size, cond_emb_size, num_heads,
+          num_res_blocks, transformer_depths)
+
+

Decoder definition of the U-Net.

+
+
+
+

Model definition

+
+

source

+
+

QC_Cond_UNet_config

+
+
 QC_Cond_UNet_config (model_features:list[int], clr_dim:int, num_clrs:int,
+                      t_emb_size:int, cond_emb_size:int,
+                      num_heads:list[int], num_res_blocks:list[int],
+                      transformer_depths:list[int])
+
+
+

source

+
+
+

QC_Cond_UNet

+
+
 QC_Cond_UNet (model_features=[32, 32, 64], clr_dim=8, num_clrs=8,
+               t_emb_size=128, cond_emb_size=512, num_heads=[8, 8, 2],
+               num_res_blocks=[2, 2, 4], transformer_depths=[1, 2, 1])
+
+

Conditional U-Net model for quantum circuits. Implemets embedd_clrs and invert_clr functions to embed and decode color-tensors.

+
+
+
+

Unitary compilation extension

+
+

source

+
+

QC_Compilation_UNet_config

+
+
 QC_Compilation_UNet_config (model_features:list[int], clr_dim:int,
+                             num_clrs:int, t_emb_size:int,
+                             cond_emb_size:int, num_heads:list[int],
+                             num_res_blocks:list[int],
+                             transformer_depths:list[int], unitary_encoder
+                             _config:genQC.models.unitary_encoder.Unitary_
+                             encoder_config)
+
+
+

source

+
+
+

QC_Compilation_UNet

+
+
 QC_Compilation_UNet (model_features=[32, 32, 64], clr_dim=8, num_clrs=8,
+                      t_emb_size=128, cond_emb_size=512, num_heads=[8, 8,
+                      2], num_res_blocks=[2, 2, 4], transformer_depths=[1,
+                      2, 1], unitary_encoder_config=None)
+
+

Extension of the QC_Cond_UNet to accept unitary conditions.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/models/unitary_encoder.html b/models/unitary_encoder.html new file mode 100644 index 0000000..65d1c86 --- /dev/null +++ b/models/unitary_encoder.html @@ -0,0 +1,739 @@ + + + + + + + + + +genQC - Encoder for unitaries + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Encoder for unitaries

+
+ + + +
+ + + + +
+ + +
+ + +
+

Model definition

+
+

source

+
+

Unitary_encoder_config

+
+
 Unitary_encoder_config (cond_emb_size:int, model_features:list[int],
+                         num_heads:int, transformer_depths:list[int],
+                         dropout:float)
+
+
+

source

+
+
+

Unitary_encoder

+
+
 Unitary_encoder (cond_emb_size, model_features=None, num_heads=8,
+                  transformer_depths=[4, 4], dropout=0.1)
+
+

Encoder for unitary conditions.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/pipeline/diffusion_pipeline.html b/pipeline/diffusion_pipeline.html new file mode 100644 index 0000000..7775160 --- /dev/null +++ b/pipeline/diffusion_pipeline.html @@ -0,0 +1,725 @@ + + + + + + + + + +genQC - Diffusion Pipeline + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Diffusion Pipeline

+
+ + + +
+ + + + +
+ + +
+ + +
+

source

+
+

DiffusionPipeline

+
+
 DiffusionPipeline (scheduler:genQC.scheduler.scheduler.Scheduler,
+                    model:torch.nn.modules.module.Module,
+                    text_encoder:torch.nn.modules.module.Module,
+                    device:torch.device, enable_guidance_train=True,
+                    guidance_train_p=0.1, cached_text_enc=True)
+
+

A Pipeline for diffusion models. Implements train and inference functions. Diffusion parameters are defined inside a Scheduler object.

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/pipeline/diffusion_pipeline_special.html b/pipeline/diffusion_pipeline_special.html new file mode 100644 index 0000000..1f9396d --- /dev/null +++ b/pipeline/diffusion_pipeline_special.html @@ -0,0 +1,745 @@ + + + + + + + + + +genQC - Diffusion Pipeline Special + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Diffusion Pipeline Special

+
+ + + +
+ + + + +
+ + +
+ + +
+

source

+
+

DiffusionPipeline_attnPadded

+
+
 DiffusionPipeline_attnPadded
+                               (scheduler:genQC.scheduler.scheduler.Schedu
+                               ler, model:torch.nn.modules.module.Module, 
+                               text_encoder:torch.nn.modules.module.Module
+                               , device:torch.device,
+                               enable_guidance_train=True,
+                               guidance_train_p=0.1, cached_text_enc=True)
+
+

A special DiffusionPipeline with attention masking.

+
+

source

+
+
+

DiffusionPipeline_Compilation

+
+
 DiffusionPipeline_Compilation
+                                (scheduler:genQC.scheduler.scheduler.Sched
+                                uler,
+                                model:torch.nn.modules.module.Module, text
+                                _encoder:torch.nn.modules.module.Module,
+                                device:torch.device,
+                                enable_guidance_train=True,
+                                guidance_train_p=0.1,
+                                cached_text_enc=True)
+
+

A special DiffusionPipeline that accounts for unitary conditions, i.e. compilation.

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/pipeline/pipeline.html b/pipeline/pipeline.html new file mode 100644 index 0000000..d9db52c --- /dev/null +++ b/pipeline/pipeline.html @@ -0,0 +1,743 @@ + + + + + + + + + +genQC - Pipeline + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Pipeline

+
+ + + +
+ + + + +
+ + +
+ + +

Basic PyTorch pipeline for general training.

+
+

Helper

+
+
+

Pipeline

+

Note, uses functions that require: python>=3.9

+
+

source

+
+

Pipeline_IO

+
+
 Pipeline_IO ()
+
+

A class providing basic IO functionality.

+
+

source

+
+
+

Pipeline

+
+
 Pipeline (model:torch.nn.modules.module.Module, device:torch.device)
+
+

A Pipeline_IO class providing basic pytorch model training functionality.

+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/platform/qcircuit_dataset_construction.html b/platform/qcircuit_dataset_construction.html new file mode 100644 index 0000000..0a38011 --- /dev/null +++ b/platform/qcircuit_dataset_construction.html @@ -0,0 +1,957 @@ + + + + + + + + + +genQC - Quantum circuit dataset construction + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Quantum circuit dataset construction

+
+ + + +
+ + + + +
+ + +
+ + +

Functions to construct a dataset. Here we define the tokenization (encoding and decoding).

+
+

Tokenizer: encoding and decoding

+
+

source

+
+

get_target_control_qubits

+
+
 get_target_control_qubits
+                            (qc:qiskit.circuit.quantumcircuit.QuantumCircu
+                            it, gate:qiskit.circuit.gate.Gate)
+
+
+

source

+
+
+

encode_circuit

+
+
 encode_circuit (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,
+                 num_of_qubits, gate_classes:dict, max_gates:int,
+                 sign_labels={'control_qubits': -1, 'target_qubits': 1},
+                 return_params=False)
+
+
+

source

+
+
+

decode_circuit

+
+
 decode_circuit (enc_tensor:torch.Tensor,
+                 gate_pool:list[qiskit.circuit.gate.Gate],
+                 place_barrier=True, sign_labels={'control_qubits': -1,
+                 'target_qubits': 1}, params_tensor=None)
+
+
+
+
+

Dataset generation

+
+

Totally random SRV circuits

+
+

source

+
+
+

get_rnd_encoded_circuit

+
+
 get_rnd_encoded_circuit (num_of_qubits, min_gates, max_gates, gate_pool,
+                          gate_classes, rng, optimized=True,
+                          return_params=False)
+
+
+

source

+
+
+

get_rnd_encoded_circuits

+
+
 get_rnd_encoded_circuits (samples, num_of_qubits=3, min_gates=3,
+                           max_gates=10, gate_pool=[<class 'qiskit.circuit
+                           .library.standard_gates.h.HGate'>, <class 'qisk
+                           it.circuit.library.standard_gates.x.CXGate'>],
+                           optimized=True, silent=False,
+                           return_params=False)
+
+
+
gate_pool=[ql.HGate, ql.CXGate, ql.CU3Gate, ql.CRXGate]
+
+print("Encode:")
+enc_t, y, params = get_rnd_encoded_circuits(samples=1, num_of_qubits=3, min_gates=6, max_gates=6, gate_pool=gate_pool, optimized=True, return_params=True)
+
+for enc_i, y_i, params_i in zip(enc_t, y, params):
+    print(f"{enc_i=}")
+    print(f"{y_i=}")
+    print(f"{params_i=}")
+
+print("Decode:")
+qc = decode_circuit(enc_t[0], gate_pool=gate_pool, params_tensor=params[0])
+display(qc.draw("mpl"))
+
+
Encode:
+enc_i=tensor([[-2,  0, -2,  3,  3,  0],
+        [ 2,  1,  2, -3, -3,  0],
+        [ 0,  0,  0,  0,  0,  1]], dtype=torch.int32)
+y_i=[2, 2, 1]
+params_i=tensor([[0.0000, 0.0000, 0.0000, 2.2710, 4.8585, 0.0000],
+        [0.0000, 0.0000, 0.0000, 0.0304, 1.1044, 0.0000],
+        [0.0000, 0.0000, 0.0000, 3.0486, 4.9504, 0.0000]])
+Decode:
+
+
+ +
+
+

+
+
+
+

source

+
+
+

gen_qc_dataset

+
+
 gen_qc_dataset (samples, num_of_qubits, min_gates, max_gates, gate_pool,
+                 optimized, silent=False)
+
+
+
+

Specific random SRV circuit

+
+

source

+
+
+

get_specific_rnd_srv_circuit

+
+
 get_specific_rnd_srv_circuit (srv, requested_length, gate_pool,
+                               max_i=2000, silent=True,
+                               fix_length_after_optimizing=True,
+                               requested_length_tolerance=0)
+
+
+
+

Unitary dataset

+
+

source

+
+
+

gen_compilation_rndGates_dataset

+
+
 gen_compilation_rndGates_dataset (samples, num_of_qubits, min_gates,
+                                   max_gates, gate_pool,
+                                   min_sub_gate_pool_cnt=1, silent=False)
+
+

Samples rnd circuit with a rnd subset of gates and return qc with gate label and unitary

+
+
gate_pool=[ql.HGate, ql.CXGate, ql.ZGate, ql.XGate, ql.CCXGate]
+
+enc_t, y, U = gen_compilation_rndGates_dataset(samples=1, num_of_qubits=3, min_gates=3, max_gates=4, gate_pool=gate_pool)
+
+np.set_printoptions(edgeitems=30, linewidth=100000, formatter=dict(float=lambda x: "%.3g" % x))
+
+print(f"\ny Label >>> {y[0]} <<<")
+print(f"\n{enc_t[0]}")
+print(f"\n{U[0]}")
+print("\nDecoded:")
+qc = decode_circuit(enc_t[0], gate_pool=gate_pool)
+display(qc.draw("mpl"))
+
+ +
+
+
generated unique circuits: 1
+
+y Label >>> Compile using: ['x'] <<<
+
+tensor([[0, 0, 0, 0],
+        [4, 0, 0, 0],
+        [0, 4, 4, 4]], dtype=torch.int32)
+
+[[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 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 1.+0.j]
+ [0.+0.j 0.+0.j 0.+0.j 0.+0.j 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 1.+0.j 0.+0.j 0.+0.j]
+ [0.+0.j 0.+0.j 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 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
+ [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 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]]
+
+Decoded:
+
+
+

+
+
+
+
+

Graph states dataset

+
+
#place all h on all bist then only cz
+
+ + +
+
+ + Back to top
+ + +
+ + + + \ No newline at end of file diff --git a/platform/qcircuit_dataset_construction_files/figure-html/cell-11-output-3.png b/platform/qcircuit_dataset_construction_files/figure-html/cell-11-output-3.png new file mode 100644 index 0000000000000000000000000000000000000000..75344cb32d97d4a0df3f094c49d0638abecd294b GIT binary patch literal 7564 zcmcgxc{r49+aHhTQAzZaN(hxgLfI8fi+$fI463^U&~?^EwP_4R(o@%{0A-ACqjxaYdB>%7kM{4LibLw$`s?7Zv{2xO0z zrm8Un@|!QXU);$Cej4}OsRAELerlF}Cf?3|aQj3C!+WjwAK&M&>%qm17}=wHXTCac5xS?v6j&b*3x~eXQf$=(G4M`vNm|9lZ(H=6!qr_cML9 zX;A(Z*%Q|TwANFpR3y&%++jF*ICy57P+0*d!`jJ6TQJ3)&!cBqz-P2_3qJ&MG1O=Z z{PJ6b^bQDQuU)(n1QH6##DfQZ=W*hMK!oZ=8IfX9N3(*jp85a(ibupOOX&RPPoJVc zjnlWLq4O3vN`gShhCOnekH|wf+t}FnA>sk|QC3Z1)(>B7kFjPxoj*K+=f~Vy|8muH zpiIxD9V@58&T*90%YpvtEbA{<=+jbhbKDdLE4(@0gckVYK{QMGYgBJC3kVsJwD)51 zzHMO#Y4uf+=POY82 zy{-sup37{5O*DH4w*MSPoj9RS)^x>l?;(uK<@d%E{4a zjuNymFo>EpvfFvshmZR3$>C?e+O&HnD=RCeSFh&Q)F^jk*6)=bkzeQHld1Qg+Ur_q zYqir60`$fvF8-YkA}!WILDi>D 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a/platform/qcircuit_evaluation.html b/platform/qcircuit_evaluation.html new file mode 100644 index 0000000..0099782 --- /dev/null +++ b/platform/qcircuit_evaluation.html @@ -0,0 +1,754 @@ + + + + + + + + + +genQC - Quantum circuit evaluation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Quantum circuit evaluation

+
+ + + +
+ + + + +
+ + +
+ + +
+

Gate count

+
+

source

+
+

sort_into_bins

+
+
 sort_into_bins (x, y, y_uniques)
+
+
+

source

+
+
+

extract_gate_number

+
+
 extract_gate_number (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,
+                      gate_pool, max_gates)
+
+
+

source

+
+
+

get_gate_stat_from_tensors

+
+
 get_gate_stat_from_tensors (tensors, gate_pool)
+
+
+

source

+
+
+

get_gate_stat_from_circuits

+
+
 get_gate_stat_from_circuits (qcs:list, gate_pool, max_gates)
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/platform/qcircuit_metrics.html b/platform/qcircuit_metrics.html new file mode 100644 index 0000000..73c7971 --- /dev/null +++ b/platform/qcircuit_metrics.html @@ -0,0 +1,772 @@ + + + + + + + + + +genQC - Quantum circuit metrics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Quantum circuit metrics

+
+ + + +
+ + + + +
+ + +
+ + +

Norms for unitary compilation.

+
+

Unitary distances

+
+

source

+
+

Unitary_FrobeniusNorm

+
+
 Unitary_FrobeniusNorm ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
a = torch.tensor([[1,2], [2, 1]]).float()
+b = torch.tensor([[2,3], [2, 2]]).float()
+
+g = Unitary_FrobeniusNorm
+g.distance(a,b)
+
+
tensor(1.5000)
+
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/platform/qcircuit_util.html b/platform/qcircuit_util.html new file mode 100644 index 0000000..2e2a340 --- /dev/null +++ b/platform/qcircuit_util.html @@ -0,0 +1,782 @@ + + + + + + + + + +genQC - Quantum circuit utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Quantum circuit utils

+
+ + + +
+ + + + +
+ + +
+ + +
+

SRV

+
+

source

+
+

get_element_matching_indices

+
+
 get_element_matching_indices (a, b)
+
+

Compares (2d) a with b. Returns the indices of b, where a element of a matches with b.

+
+

source

+
+
+

get_entanglement_bins

+
+
 get_entanglement_bins (num_of_qubits)
+
+

Returns all SRV sorted in entangle bins which correspond to a number of entangled qubits.

+
+
for srvs,label in zip(*get_entanglement_bins(5)):
+    print(label, ":", srvs)
+
+
0 qubit entangled : [[1, 1, 1, 1, 1]]
+2 qubit entangled : [[1, 1, 1, 2, 2], [1, 1, 2, 1, 2], [1, 1, 2, 2, 1], [1, 2, 1, 1, 2], [1, 2, 1, 2, 1], [1, 2, 2, 1, 1], [2, 1, 1, 1, 2], [2, 1, 1, 2, 1], [2, 1, 2, 1, 1], [2, 2, 1, 1, 1]]
+3 qubit entangled : [[1, 1, 2, 2, 2], [1, 2, 1, 2, 2], [1, 2, 2, 1, 2], [1, 2, 2, 2, 1], [2, 1, 1, 2, 2], [2, 1, 2, 1, 2], [2, 1, 2, 2, 1], [2, 2, 1, 1, 2], [2, 2, 1, 2, 1], [2, 2, 2, 1, 1]]
+4 qubit entangled : [[1, 2, 2, 2, 2], [2, 1, 2, 2, 2], [2, 2, 1, 2, 2], [2, 2, 2, 1, 2], [2, 2, 2, 2, 1]]
+5 qubit entangled : [[2, 2, 2, 2, 2]]
+
+
+ + +
+
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/platform/simulation/qcircuit_sim.html b/platform/simulation/qcircuit_sim.html new file mode 100644 index 0000000..488d782 --- /dev/null +++ b/platform/simulation/qcircuit_sim.html @@ -0,0 +1,951 @@ + + + + + + + + + +genQC - Qiskit: quantum circuit simulation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Qiskit: quantum circuit simulation

+
+ + + +
+ + + + +
+ + +
+ + +
+

Circuit

+
+

source

+
+

get_number_of_gate_params

+
+
 get_number_of_gate_params (gate_cls)
+
+
+

source

+
+
+

gate_pool_to_gate_classes

+
+
 gate_pool_to_gate_classes (gate_pool:list[qiskit.circuit.gate.Gate])
+
+

Creates a vocabulary from a gate pool.

+
+

source

+
+
+

schmidt_rank_vector

+
+
 schmidt_rank_vector
+                      (densityMatrix:qiskit.quantum_info.states.densitymat
+                      rix.DensityMatrix)
+
+

Return the SRV of a qi.DensityMatrix.

+
+

source

+
+
+

rnd_circuit

+
+
 rnd_circuit (num_of_qubits, num_of_gates,
+              gate_pool:list[qiskit.circuit.gate.Gate], rng)
+
+

Create a random circuit.

+
+

source

+
+
+

optimize_circuit

+
+
 optimize_circuit (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,
+                   gate_pool:list[qiskit.circuit.gate.Gate],
+                   optimization_level=2)
+
+

Use qiskit.compiler.transpile to optimize a circuit.

+
+
gs = [ql.HGate, ql.CXGate, ql.CRXGate]
+qc = rnd_circuit(num_of_qubits=3, num_of_gates=8, gate_pool=gs, rng=np.random.default_rng())
+svr = schmidt_rank_vector(qi.DensityMatrix(qc))
+display(f"Rand: svr={svr}    num_gates={len(qc.data)}", qc.draw('mpl'))
+
+qc = optimize_circuit(qc, gs)
+svr = schmidt_rank_vector(qi.DensityMatrix(qc))
+display(f"Opti: svr={svr}    num_gates={len(qc.data)}", qc.draw('mpl'))
+
+
'Rand: svr=[1, 1, 1]    num_gates=8'
+
+
+

+
+
+
'Opti: svr=[1, 1, 1]    num_gates=8'
+
+
+

+
+
+
+

source

+
+
+

plot_svr_stat

+
+
 plot_svr_stat (num_of_qubits, min_gates, max_gates, gs, samples,
+                sort=False, opt=True, rng=Generator(PCG64) at
+                0x7F4E2117AC80)
+
+

SRV distrubtion for random sampling

+
+
# gs = [ql.HGate, ql.SGate, ql.TGate, ql.CXGate]  # approx universal set
+# gs = [ql.HGate, ql.CCXGate]                     # approx universal set
+gs = [ql.HGate, ql.CXGate]
+
+plot_svr_stat(num_of_qubits=3, min_gates=6, max_gates=8, gs=gs, samples=int(1e3),  rng=np.random.default_rng())
+
+
[1, 1, 1]: 58.2%
+[2, 2, 1]: 11.5%
+[2, 2, 2]: 11.3%
+[1, 2, 2]: 9.6%
+[2, 1, 2]: 9.4%
+
+
+

+
+
+
+
+
+

SVR Test cases

+
+
def test_srv(system_dims, init, target):
+    vec = qi.Statevector(init, dims=system_dims)
+    vec *= 1/np.sqrt(vec.trace())
+    srv = schmidt_rank_vector(qi.DensityMatrix(vec)) 
+    assert srv == target, f"srv: {srv}"
+    print(f"passed test, svr: {srv}")
+    display(vec.draw('latex', prefix='|\\psi\\rangle = '))
+
+
+
#---------------- |0+> = |00>+|01>
+system_dims = (2,2)
+init = np.zeros(np.prod(system_dims), dtype=complex)
+init[0] = 1
+init[1] = 1
+test_srv(system_dims, init, [1, 1])
+
+#----------------Bell, |00>+|11>
+system_dims = (2,2)
+init = np.zeros(np.prod(system_dims), dtype=complex)
+init[0] = 1
+init[3] = 1
+test_srv(system_dims, init, [2, 2])
+  
+#----------------GHZ, |000>+|111>
+system_dims = (2,2,2)
+init = np.zeros(np.prod(system_dims), dtype=complex)
+init[0] = 1
+init[7] = 1
+test_srv(system_dims, init, [2,2,2])
+ 
+#----------------Sym, |000>+|111>+|222>
+system_dims = (3,3,3)
+init = np.zeros(np.prod(system_dims), dtype=complex)
+init[0]  = 1
+init[13] = 1
+init[26] = 1
+test_srv(system_dims, init, [3,3,3])
+      
+#----------------Wikipedia example, |000>+|101>+|210>+|311>
+system_dims = (4,4,4)
+init = np.zeros(np.prod(system_dims), dtype=complex)
+init[0]  = 1
+init[17] = 1
+init[36] = 1
+init[53] = 1
+test_srv(system_dims, init, [2, 2, 4])
+
+
passed test, svr: [1, 1]
+passed test, svr: [2, 2]
+passed test, svr: [2, 2, 2]
+passed test, svr: [3, 3, 3]
+passed test, svr: [2, 2, 4]
+
+
+

\[|\psi\rangle = \frac{\sqrt{2}}{2} |00\rangle+\frac{\sqrt{2}}{2} |01\rangle\]

+
+
+

\[|\psi\rangle = \frac{\sqrt{2}}{2} |00\rangle+\frac{\sqrt{2}}{2} |11\rangle\]

+
+
+

\[|\psi\rangle = \frac{\sqrt{2}}{2} |000\rangle+\frac{\sqrt{2}}{2} |111\rangle\]

+
+
+

$$\[\begin{align} + +|\psi\rangle = +\begin{bmatrix} +\frac{\sqrt{3}}{3} & 0 & 0 & 0 & \cdots & 0 & 0 & \frac{\sqrt{3}}{3} \\ +\end{bmatrix} +\\ +\text{dims=(3, 3, 3)} +\end{align}\]$$

+
+
+

$$\[\begin{align} + +|\psi\rangle = +\begin{bmatrix} +\frac{1}{2} & 0 & 0 & 0 & \cdots & 0 & 0 & 0 \\ +\end{bmatrix} +\\ +\text{dims=(4, 4, 4)} +\end{align}\]$$

+
+
+ + +
+ + Back to top
+ +
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zOnheKsD<(I@hj_8KO(dry&9qJG|tZvHjPNj^$uFb#(5nb9XG%4Leag2F4CU!A-j)j zC@6$L@l5}9P6zIi2NNUdTc)U_1d$^zMgz)JOl)jqeEejk51dGVR#p;(0~e-0&@-ah zWnc(h6VEjJveC21xdiv`jv*iq(o`jvui=sITqUS28!M|nI3+gM=IZL|G~g~5XCdrk z6R|fsFTZ`y&8<8A{dcgi=t5QjpGQD}H(ejoU8xbNO6uw|F!r7-URfcN@?FQ + + + + + + + + +genQC - Printing functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + +
+ +
+
+

Printing functions

+
+ + + +
+ + + + +
+ + +
+ + +

Advanced printing functions.

+
+

source

+
+

display_colums

+
+
 display_colums (display_list, num_col=3)
+
+
+
a = [torch.rand((3, 3))]*3
+a
+
+
[tensor([[0.6266, 0.3672, 0.3988],
+         [0.6061, 0.2596, 0.4855],
+         [0.3177, 0.8166, 0.3100]]),
+ tensor([[0.6266, 0.3672, 0.3988],
+         [0.6061, 0.2596, 0.4855],
+         [0.3177, 0.8166, 0.3100]]),
+ tensor([[0.6266, 0.3672, 0.3988],
+         [0.6061, 0.2596, 0.4855],
+         [0.3177, 0.8166, 0.3100]])]
+
+
+

v.s.

+
+
display_colums(a) # works only in notebook
+
+ +
+
+
+

source

+
+
+

ndarray_to_latex

+
+
 ndarray_to_latex (arr)
+
+

Returns a LaTeX {pmatrix*}[r] as a string

+
+

source

+
+
+

tensor_to_latex

+
+
 tensor_to_latex (tensor)
+
+

Returns a LaTeX {pmatrix*}[r] as a string

+
+
tex = tensor_to_latex(torch.full((3,3), 2))
+print(tex)
+
+
\begin{pmatrix*}[r]
+  2 & 2 & 2\\
+  2 & 2 & 2\\
+  2 & 2 & 2\\
+\end{pmatrix*}
+
+
+
+

source

+
+ + + + Back to top
+ + +
+ + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..878f065 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://FlorianFuerrutter.github.io/genQC/sitemap.xml diff --git a/scheduler/scheduler.html b/scheduler/scheduler.html new file mode 100644 index 0000000..d46f752 --- /dev/null +++ b/scheduler/scheduler.html @@ -0,0 +1,722 @@ + + + + + + + + + +genQC - Scheduler + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Scheduler

+
+ + + +
+ + + + +
+ + +
+ + +

Base class for schedulers.

+
+

source

+
+

Scheduler

+
+
 Scheduler ()
+
+

Base class for all diffusion schedulers

+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/scheduler/scheduler_ddim.html b/scheduler/scheduler_ddim.html new file mode 100644 index 0000000..019f1d0 --- /dev/null +++ b/scheduler/scheduler_ddim.html @@ -0,0 +1,736 @@ + + + + + + + + + +genQC - DDIMScheduler + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

DDIMScheduler

+
+ + + +
+ + + + +
+ + +
+ + +

Denoising diffusion implicit models (DDIM).

+
+

source

+
+

DDIMScheduler

+
+
 DDIMScheduler (device:Union[str,torch.device],
+                num_train_timesteps:int=1000, beta_start:float=0.0001,
+                beta_end:float=0.02, beta_schedule:str='linear',
+                input_perturbation=0.1, eta:float=0)
+
+

A Scheduler implementing (DDIM).

+
+

source

+
+
+

DDIMSchedulerOutput

+
+
 DDIMSchedulerOutput (prev_sample:torch.FloatTensor,
+                      pred_original_sample:Optional[torch.FloatTensor]=Non
+                      e)
+
+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/scheduler/scheduler_ddpm.html b/scheduler/scheduler_ddpm.html new file mode 100644 index 0000000..dfde7ec --- /dev/null +++ b/scheduler/scheduler_ddpm.html @@ -0,0 +1,736 @@ + + + + + + + + + +genQC - DDPMScheduler + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

DDPMScheduler

+
+ + + +
+ + + + +
+ + +
+ + +

Denoising diffusion probabilistic models (DDPM): reverse beta is fixed and diagonal.

+
+

source

+
+

DDPMScheduler

+
+
 DDPMScheduler (device:Union[str,torch.device],
+                num_train_timesteps:int=1000, beta_start:float=0.0001,
+                beta_end:float=0.02, beta_schedule:str='linear',
+                input_perturbation=0.1)
+
+

A Scheduler implementing (DDPM)

+
+

source

+
+
+

DDPMSchedulerOutput

+
+
 DDPMSchedulerOutput (prev_sample:torch.FloatTensor,
+                      pred_original_sample:Optional[torch.FloatTensor]=Non
+                      e)
+
+ + +
+ + Back to top
+ +
+ + + + \ No newline at end of file diff --git a/search.json b/search.json new file mode 100644 index 0000000..98d725c --- /dev/null +++ b/search.json @@ -0,0 +1,639 @@ +[ + { + "objectID": "printing.html", + "href": "printing.html", + "title": "Printing functions", + "section": "", + "text": "Advanced printing functions.\n\nsource\n\ndisplay_colums\n\n display_colums (display_list, num_col=3)\n\n\na = [torch.rand((3, 3))]*3\na\n\n[tensor([[0.6266, 0.3672, 0.3988],\n [0.6061, 0.2596, 0.4855],\n [0.3177, 0.8166, 0.3100]]),\n tensor([[0.6266, 0.3672, 0.3988],\n [0.6061, 0.2596, 0.4855],\n [0.3177, 0.8166, 0.3100]]),\n tensor([[0.6266, 0.3672, 0.3988],\n [0.6061, 0.2596, 0.4855],\n [0.3177, 0.8166, 0.3100]])]\n\n\nv.s.\n\ndisplay_colums(a) # works only in notebook\n\n\n\n\n\nsource\n\n\nndarray_to_latex\n\n ndarray_to_latex (arr)\n\nReturns a LaTeX {pmatrix*}[r] as a string\n\nsource\n\n\ntensor_to_latex\n\n tensor_to_latex (tensor)\n\nReturns a LaTeX {pmatrix*}[r] as a string\n\ntex = tensor_to_latex(torch.full((3,3), 2))\nprint(tex)\n\n\\begin{pmatrix*}[r]\n 2 & 2 & 2\\\\\n 2 & 2 & 2\\\\\n 2 & 2 & 2\\\\\n\\end{pmatrix*}\n\n\n\nsource\n\n\nprint_markdown\n\n print_markdown (text, print_raw=False)\n\n\nprint_markdown(\"$\\sqrt{2}$, *Test text*\")\n\n\\(\\sqrt{2}\\), Test text\n\n\n\nprint_markdown(f\"${tex}$\")\n\n\\(\\begin{pmatrix*}[r] 2 & 2 & 2\\\\ 2 & 2 & 2\\\\ 2 & 2 & 2\\\\ \\end{pmatrix*}\\)\n\n\n\nsource\n\n\nprint_table\n\n print_table (col_headings:list, data:<built-infunctionarray>,\n row_headings=None, print_raw=False)\n\nPrint a table:\n\nh = [\"head1\", \"head2\", \"head3\"]\nr = [\"sample\", \"dataset\"]\nd = np.random.rand(2, 3)\nprint_table(h, d, r)\n\n\n\n\n\nhead1\nhead2\nhead3\n\n\n\n\nsample\n0.32\n0.23\n0.27\n\n\ndataset\n0.74\n0.41\n0.00\n\n\n\n\n\n\n\n\n\n Back to top" + }, + { + "objectID": "inference/infer_misc.html", + "href": "inference/infer_misc.html", + "title": "Inference miscellaneous functions", + "section": "", + "text": "source\n\n\n\n get_rnd_gatepool_subset (gate_pool, min_sub_gate_pool_cnt=2)" + }, + { + "objectID": "inference/infer_misc.html#misc", + "href": "inference/infer_misc.html#misc", + "title": "Inference miscellaneous functions", + "section": "", + "text": "source\n\n\n\n get_rnd_gatepool_subset (gate_pool, min_sub_gate_pool_cnt=2)" + }, + { + "objectID": "inference/infer_misc.html#convertion", + "href": "inference/infer_misc.html#convertion", + "title": "Inference miscellaneous functions", + "section": "Convertion", + "text": "Convertion\n\nsource\n\nconvert_tensors_to_circuits\n\n convert_tensors_to_circuits (out_tensor, gate_pool, place_barrier=False)" + }, + { + "objectID": "inference/infer_gate_hist.html", + "href": "inference/infer_gate_hist.html", + "title": "Inference gate distribution", + "section": "", + "text": "source\n\n\n\n get_tensor_gate_length (clr_tensor, padding_token=0)\n\nCareful with padding tokens!\n\nsource\n\n\n\n\n get_circuit_gate_length (qcs)" + }, + { + "objectID": "inference/infer_gate_hist.html#gate-length", + "href": "inference/infer_gate_hist.html#gate-length", + "title": "Inference gate distribution", + "section": "", + "text": "source\n\n\n\n get_tensor_gate_length (clr_tensor, padding_token=0)\n\nCareful with padding tokens!\n\nsource\n\n\n\n\n get_circuit_gate_length (qcs)" + }, + { + "objectID": "dataset/qc_dataset.html", + "href": "dataset/qc_dataset.html", + "title": "Quantum circuit dataset", + "section": "", + "text": "Dataset for quantum circuits.\n\nsource\n\nQc_Config_Dataset_config\n\n Qc_Config_Dataset_config (store_dict:dict, optimized:bool,\n dataset_to_gpu:bool, random_samples:int,\n num_of_qubits:int, min_gates:int,\n max_gates:int, gate_pool:list[str])\n\n\nsource\n\n\nQc_Config_Dataset\n\n Qc_Config_Dataset (device:torch.device=device(type='cpu'), **parameters)\n\nDataset for quantum circuits, access gate_pool directly and all other paras with .params_config\n\ninit = {k:None for k in Qc_Config_Dataset.req_params}\ninit[\"gate_pool\"] = [\"qiskit.circuit.library.standard_gates.h.HGate\",\n \"qiskit.circuit.library.standard_gates.x.CXGate\"]\ninit[\"store_dict\"] = {\"x\":\"tensor\", \"y\":\"tensor_list\"}\n\na = Qc_Config_Dataset(**init)\na.get_config()\n\n{'target': '__main__.Qc_Config_Dataset',\n 'device': 'cpu',\n 'comment': '',\n 'save_path': None,\n 'save_datetime': '11/09/2023 18:48:57',\n 'params': Qc_Config_Dataset_config(store_dict={'x': 'tensor', 'y': 'tensor_list'}, optimized=None, dataset_to_gpu=None, random_samples=None, num_of_qubits=None, min_gates=None, max_gates=None, gate_pool=['qiskit.circuit.library.standard_gates.h.HGate', 'qiskit.circuit.library.standard_gates.x.CXGate'])}\n\n\n\n\n\n\n Back to top" + }, + { + "objectID": "dataset/mixed_cached_qc_dataset.html", + "href": "dataset/mixed_cached_qc_dataset.html", + "title": "Mixed cached dataset", + "section": "", + "text": "Dataset that combines and handles multiple cached datasets, e.g. for multiple qubits. Here we also handle paddings.\n\nsource\n\nMixed_Cached_OpenClip_Dataset_config\n\n Mixed_Cached_OpenClip_Dataset_config (store_dict:dict, optimized:bool,\n dataset_to_gpu:bool,\n random_samples:int,\n num_of_qubits:int, min_gates:int,\n max_gates:int, gate_pool:list[str],\n pad_constant:int, collate_fn:str,\n bucket_batch_size:int,\n num_down_scales:int)\n\n\nsource\n\n\nMixed_Cached_OpenClip_Dataset\n\n Mixed_Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),\n **parameters)\n\nDataset that uses multiple cached dataset and combines them with padding, either i) Bucket or ii) Max. Also provides a corresponding collate_fn for training.\n\n\n\n\n Back to top" + }, + { + "objectID": "scheduler/scheduler.html", + "href": "scheduler/scheduler.html", + "title": "Scheduler", + "section": "", + "text": "Base class for schedulers.\n\nsource\n\nScheduler\n\n Scheduler ()\n\nBase class for all diffusion schedulers\n\n\n\n\n Back to top" + }, + { + "objectID": "scheduler/scheduler_ddim.html", + "href": "scheduler/scheduler_ddim.html", + "title": "DDIMScheduler", + "section": "", + "text": "Denoising diffusion implicit models (DDIM).\n\nsource\n\nDDIMScheduler\n\n DDIMScheduler (device:Union[str,torch.device],\n num_train_timesteps:int=1000, beta_start:float=0.0001,\n beta_end:float=0.02, beta_schedule:str='linear',\n input_perturbation=0.1, eta:float=0)\n\nA Scheduler implementing (DDIM).\n\nsource\n\n\nDDIMSchedulerOutput\n\n DDIMSchedulerOutput (prev_sample:torch.FloatTensor,\n pred_original_sample:Optional[torch.FloatTensor]=Non\n e)\n\n\n\n\n\n Back to top" + }, + { + "objectID": "util.html", + "href": "util.html", + "title": "Util", + "section": "", + "text": "Miscellaneous util code." + }, + { + "objectID": "util.html#memory-utils", + "href": "util.html#memory-utils", + "title": "Util", + "section": "Memory utils", + "text": "Memory utils\n\nsource\n\nMemoryCleaner\n\n MemoryCleaner ()\n\nCLass with static methods to clean (gpu) memory." + }, + { + "objectID": "util.html#python-utils", + "href": "util.html#python-utils", + "title": "Util", + "section": "Python utils", + "text": "Python utils\n\nsource\n\nvirtual\n\n virtual (f:<built-infunctioncallable>)\n\nDecorator to enfore subclass method implementations and raises error at method calls.\n\nclass A():\n def p1(self, x): print(\"A p1\", x)\n \n @virtual\n def p2(self, x): pass\n \nclass B(A):\n def p3(self, x): print(\"B p2\", x)\n \nb = B()\nb.p1(1)\ntry:\n b.p2(1)\nexcept BaseException as e:\n print(\"Exception that would be raised: \", e)\n\nA p1 1\nException that would be raised: Virtual method p2 needs to be implemented by subclass B." + }, + { + "objectID": "util.html#torch-utils", + "href": "util.html#torch-utils", + "title": "Util", + "section": "Torch utils", + "text": "Torch utils\n\nsource\n\nDataLoaders\n\n DataLoaders (*dls:list[torch.utils.data.dataloader.DataLoader])\n\nCombines train and valid DataLoader.\n\nsource\n\n\ninfer_torch_device\n\n infer_torch_device ()\n\n\ninfer_torch_device()\n\n[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\n\n\ndevice(type='cuda')\n\n\n\nsource\n\n\nnumber_of_paramters\n\n number_of_paramters (model:torch.nn.modules.module.Module)\n\n\nsource\n\n\nscale_tensor\n\n scale_tensor (t:torch.Tensor)\n\n[-1,1] to [0,1]\n\nsource\n\n\nnormalize_tensor\n\n normalize_tensor (t:torch.Tensor)\n\n[0,1] to [-1,1]" + }, + { + "objectID": "util.html#plot-utils", + "href": "util.html#plot-utils", + "title": "Util", + "section": "Plot utils", + "text": "Plot utils\n\nsource\n\nsaveSvg\n\n saveSvg (filename)\n\n\nsource\n\n\nsavePng\n\n savePng (filename)\n\n\nsource\n\n\nsavePdf\n\n savePdf (filename)\n\n\nsource\n\n\nplot_image_grid\n\n plot_image_grid (imgs:Union[list,<built-infunctionarray>,torch.Tensor],\n labels:list=None, labels_fs='medium', figsize=(16, 4),\n cols=8, cmap='Greys', show_colorbar=False,\n **imshow_kwargs)\n\n\nn = 6\nplot_image_grid(torch.randn((n,28,28,1)), [f\"label {i}\" for i in range(n)])\n\n\n\n\n\nsource\n\n\nlatents_to_pil\n\n latents_to_pil (latents:torch.Tensor, channels=None)" + }, + { + "objectID": "models/frozen_open_clip.html", + "href": "models/frozen_open_clip.html", + "title": "Frozen OpenCLIP", + "section": "", + "text": "source\n\n\n\n FrozenOpenCLIPEmbedder_config (arch:str, version:str, device:str,\n max_length:int, freeze:bool, layer:str)\n\n\nsource\n\n\n\n\n FrozenOpenCLIPEmbedder (arch='ViT-H-14', version='laion2b_s32b_b79k',\n device='cpu', max_length=77, freeze=True,\n layer='penultimate')\n\nLoads and freezes the OpenCLIP transformer encoder for text prompts.\n\na = FrozenOpenCLIPEmbedder()\n\n\np=\"[1, 2, 2]\"\na.tokenize_and_push_to_device(p)\n\ntensor([[49406, 314, 272, 267, 273, 267, 273, 316, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n\n\n\na.tokenize_and_push_to_device(\"\").shape\n\ntorch.Size([1, 77])\n\n\n\nc = a.tokenize_and_push_to_device([\"1,1,2\", \"2,2,2\"])\nenc = a(c)\nenc.shape, enc\n\n(torch.Size([2, 77, 1024]),\n tensor([[[-0.3134, -0.4476, -0.0082, ..., 0.2542, -0.0324, -0.2960],\n [ 0.0668, -1.2381, 0.9908, ..., 0.1785, 0.1592, -0.4320],\n [ 0.6988, -0.2168, -1.2912, ..., 2.1063, -0.0302, -0.5666],\n ...,\n [ 0.4703, -1.4072, -0.4847, ..., -0.1257, -0.1650, 0.1206],\n [ 0.5117, -1.3949, -0.4672, ..., -0.4288, -0.2166, 0.2904],\n [ 0.1480, -2.1998, -1.1187, ..., 0.0823, -0.4157, 0.6236]],\n \n [[-0.3134, -0.4476, -0.0082, ..., 0.2542, -0.0324, -0.2960],\n [-0.1180, -1.6322, 1.2987, ..., -0.1378, -0.1529, -0.3377],\n [-0.7251, -0.8167, -0.9966, ..., 2.2262, -0.2325, -0.0138],\n ...,\n [ 0.3887, -1.3395, -0.5868, ..., -0.1621, -0.0594, 0.1253],\n [ 0.4360, -1.3350, -0.5684, ..., -0.4643, -0.1131, 0.2847],\n [ 0.1691, -2.1725, -1.1441, ..., 0.0633, -0.3175, 0.7041]]]))\n\n\n\nopen_clip.decode(c[1])\n\n'<start_of_text>2 , 2 , 2 <end_of_text>!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'" + }, + { + "objectID": "models/frozen_open_clip.html#clip-model", + "href": "models/frozen_open_clip.html#clip-model", + "title": "Frozen OpenCLIP", + "section": "", + "text": "source\n\n\n\n FrozenOpenCLIPEmbedder_config (arch:str, version:str, device:str,\n max_length:int, freeze:bool, layer:str)\n\n\nsource\n\n\n\n\n FrozenOpenCLIPEmbedder (arch='ViT-H-14', version='laion2b_s32b_b79k',\n device='cpu', max_length=77, freeze=True,\n layer='penultimate')\n\nLoads and freezes the OpenCLIP transformer encoder for text prompts.\n\na = FrozenOpenCLIPEmbedder()\n\n\np=\"[1, 2, 2]\"\na.tokenize_and_push_to_device(p)\n\ntensor([[49406, 314, 272, 267, 273, 267, 273, 316, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n\n\n\na.tokenize_and_push_to_device(\"\").shape\n\ntorch.Size([1, 77])\n\n\n\nc = a.tokenize_and_push_to_device([\"1,1,2\", \"2,2,2\"])\nenc = a(c)\nenc.shape, enc\n\n(torch.Size([2, 77, 1024]),\n tensor([[[-0.3134, -0.4476, -0.0082, ..., 0.2542, -0.0324, -0.2960],\n [ 0.0668, -1.2381, 0.9908, ..., 0.1785, 0.1592, -0.4320],\n [ 0.6988, -0.2168, -1.2912, ..., 2.1063, -0.0302, -0.5666],\n ...,\n [ 0.4703, -1.4072, -0.4847, ..., -0.1257, -0.1650, 0.1206],\n [ 0.5117, -1.3949, -0.4672, ..., -0.4288, -0.2166, 0.2904],\n [ 0.1480, -2.1998, -1.1187, ..., 0.0823, -0.4157, 0.6236]],\n \n [[-0.3134, -0.4476, -0.0082, ..., 0.2542, -0.0324, -0.2960],\n [-0.1180, -1.6322, 1.2987, ..., -0.1378, -0.1529, -0.3377],\n [-0.7251, -0.8167, -0.9966, ..., 2.2262, -0.2325, -0.0138],\n ...,\n [ 0.3887, -1.3395, -0.5868, ..., -0.1621, -0.0594, 0.1253],\n [ 0.4360, -1.3350, -0.5684, ..., -0.4643, -0.1131, 0.2847],\n [ 0.1691, -2.1725, -1.1441, ..., 0.0633, -0.3175, 0.7041]]]))\n\n\n\nopen_clip.decode(c[1])\n\n'<start_of_text>2 , 2 , 2 <end_of_text>!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'" + }, + { + "objectID": "models/frozen_open_clip.html#cached-model", + "href": "models/frozen_open_clip.html#cached-model", + "title": "Frozen OpenCLIP", + "section": "Cached model", + "text": "Cached model\nModel takes now also (batched) scalar int values that are defined to unique conditions like \\([1,2,2]=4\\). If input is now such int the output is the cached pre-embedded tensor. If a non int, like a token string is passed we just do the normal embedding live.\n\nsource\n\nCachedFrozenOpenCLIPEmbedder\n\n CachedFrozenOpenCLIPEmbedder (arch='ViT-H-14',\n version='laion2b_s32b_b79k', device='cpu',\n max_length=77, freeze=True,\n layer='penultimate')\n\nAdds caching support to FrozenOpenCLIPEmbedder.\n\na = CachedFrozenOpenCLIPEmbedder()\np = [\"1,1,2\", \"2,2,2\"]\n\na.generate_cache(p)\n\n\n\n\n[INFO]: caching trying to allocate memory (2, 77, 1024) on cpu approx. 0.001 GB\n\n\n\nc_cached = torch.tensor([0,0,1], device=a.device)\nc_uncached = a.tokenize_and_push_to_device([\"1,1,2\", \"1,1,2\", \"2,2,2\"])\n\nenc_cached = a(c_cached)\nenc_uncached = a(c_uncached)\n\nenc_cached.shape, enc_uncached.shape, torch.allclose(enc_cached, enc_uncached, atol=1e-5)\n\n(torch.Size([3, 77, 1024]), torch.Size([3, 77, 1024]), True)" + }, + { + "objectID": "models/unet_qc.html", + "href": "models/unet_qc.html", + "title": "Conditional qc-UNet", + "section": "", + "text": "Quantum circuit U-Net architecture predicting the noise for noisy quantum circuits." + }, + { + "objectID": "models/unet_qc.html#blocks", + "href": "models/unet_qc.html#blocks", + "title": "Conditional qc-UNet", + "section": "Blocks", + "text": "Blocks\n\nsource\n\nUNet_block\n\n UNet_block (ch_in, ch_out, t_emb_size, cond_emb_size, num_heads=8,\n num_res_blocks=1, transformer_depth=1)\n\nThe basic block of the U-Net. Is conditioned via cross-attention in SpatialTransformer and addition of the time ebedding in ResBlock2D_Conditional.\n\nsource\n\n\nEncoder\n\n Encoder (model_features, t_emb_size, cond_emb_size, num_heads,\n num_res_blocks, transformer_depths)\n\nEncoder definition of the U-Net.\n\nsource\n\n\nDecoder\n\n Decoder (model_features, t_emb_size, cond_emb_size, num_heads,\n num_res_blocks, transformer_depths)\n\nDecoder definition of the U-Net." + }, + { + "objectID": "models/unet_qc.html#model-definition", + "href": "models/unet_qc.html#model-definition", + "title": "Conditional qc-UNet", + "section": "Model definition", + "text": "Model definition\n\nsource\n\nQC_Cond_UNet_config\n\n QC_Cond_UNet_config (model_features:list[int], clr_dim:int, num_clrs:int,\n t_emb_size:int, cond_emb_size:int,\n num_heads:list[int], num_res_blocks:list[int],\n transformer_depths:list[int])\n\n\nsource\n\n\nQC_Cond_UNet\n\n QC_Cond_UNet (model_features=[32, 32, 64], clr_dim=8, num_clrs=8,\n t_emb_size=128, cond_emb_size=512, num_heads=[8, 8, 2],\n num_res_blocks=[2, 2, 4], transformer_depths=[1, 2, 1])\n\nConditional U-Net model for quantum circuits. 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[Paper]" + }, + { + "objectID": "index.html#the-codebase", + "href": "index.html#the-codebase", + "title": "genQC · Generative Quantum Circuits", + "section": "The codebase", + "text": "The codebase\nAll weights and functions are contained within this repo. For the CLIP model weights we use the OpenCLIP library, which will download (and cache) the CLIP model on first usage of our pipeline. In case you prefer reading a documentation rather than notebooks or code see florianfuerrutter.github.io/genQC/.\nThe repo inlcudes:\n\nsaves/ the configs and weights of the pre-trained models.\ngenQC/ a full release of our used diffusion pipeline.\nsrc/examples examples how to reproduce some figures of the Paper.\nsrc/ the source notebooks for nbdev." + }, + { + "objectID": "index.html#examples", + "href": "index.html#examples", + "title": "genQC · Generative Quantum Circuits", + "section": "Examples", + "text": "Examples\n\nMinimal example\nA minimal example to generate a 5 qubit circuit conditioned on a SRV of \\([1,1,1,2,2]\\):\n\nfrom genQC.pipeline.diffusion_pipeline import DiffusionPipeline\nfrom genQC.inference.infer_srv import generate_srv_tensors, convert_tensors_to_srvs\n\nmodel_path = \"../saves/qc_unet_config_SRV_3to8_qubit/\"\npipeline = DiffusionPipeline.from_config_file(model_path, \"cpu\") \npipeline.scheduler.set_timesteps(20) \n\nout_tensor = generate_srv_tensors(pipeline, \"Generate SRV: [1,1,1,2,2]\", samples=1, system_size=5, num_of_qubits=5, max_gates=16, g=7.5) \nqc_list, _, svr_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool)\n\n[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cpu.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cpu.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.\n\n\n\nprint(f\"is SRV {svr_list[0]}\")\nqc_list[0].draw(\"mpl\")\n\nis SRV [1, 1, 1, 2, 2]\n\n\n\n\n\n\n\nIncluded examples\nExamples are provided in the directory src/examples/.\n\n0_hello_circuit: how to sample a circuit (conditioned on a SRV)\n1_editing_and_masking: presents editing and masking\n2_unitary_compilation: compile unitariy and transpile circuit" + }, + { + "objectID": "index.html#installation", + "href": "index.html#installation", + "title": "genQC · Generative Quantum Circuits", + "section": "Installation:", + "text": "Installation:\n\n1. Clone\ngit clone https://github.com/FlorianFuerrutter/genQC.git\ncd genQC\n\n\n2. Install genQC\nThis library is build using jupyter notebooks and nbdev. To install the library use in the clone directory:\npip install -e .\nNote, this will install missing requirements automatically: [torch numpy matplotlib scipy pandas omegaconf qiskit tqdm joblib open_clip_torch ipywidgets pylatexenc]. You may want to install some of them manually beforehand, e.g. pytorch for specific cuda support pytorch.org/get-started/locally.\n\n\n3. Run example\nYou can run the provided 0_hello_circuit example to test your installation." + }, + { + "objectID": "index.html#license", + "href": "index.html#license", + "title": "genQC · Generative Quantum Circuits", + "section": "License", + "text": "License\nThe code and weights in this repository are released under the MIT License." + }, + { + "objectID": "index.html#bibtex", + "href": "index.html#bibtex", + "title": "genQC · Generative Quantum Circuits", + "section": "BibTeX", + "text": "BibTeX\nWe kindly ask you to cite our paper if any of the previous material was useful for your work.\n@article{fuerrutter2023quantum,\n title={Quantum circuit synthesis with diffusion models}, \n author={Florian Fürrutter and Gorka Muñoz-Gil and Hans J. Briegel},\n year={2023},\n eprint={2311.02041},\n publisher = {arXiv},\n archivePrefix={arXiv},\n primaryClass={quant-ph},\n url = {https://arxiv.org/abs/2311.02041},\n doi = {/10.48550/arXiv.2311.02041},\n}" + }, + { + "objectID": "examples/hello_circuit.html", + "href": "examples/hello_circuit.html", + "title": "Generate a circuit", + "section": "", + "text": "A minimal example to generate a circuit. We load a pre-trained (SRV, 3 to 8 qubit) model and condition on a given Schmidt-Rank-Vector (SRV).\nfrom genQC.imports import *\nfrom genQC.pipeline.diffusion_pipeline import DiffusionPipeline\nimport genQC.inference.infer_srv as infer_srv\nimport genQC.util as util\ndevice = util.infer_torch_device() # use cuda if we can\nutil.MemoryCleaner.purge_mem() # clean existing memory alloc\n\n[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\ndevice\n\ndevice(type='cuda')" + }, + { + "objectID": "examples/hello_circuit.html#setup-and-load", + "href": "examples/hello_circuit.html#setup-and-load", + "title": "Generate a circuit", + "section": "Setup and load", + "text": "Setup and load\nLoad the pre-trained model\n\nmodel_path = \"../../saves/qc_unet_config_SRV_3to8_qubit/\"\npipeline = DiffusionPipeline.from_config_file(model_path, device)\n\n[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.\n\n\nCheck on what gates the model was trained\n\npipeline.gate_pool\n\n[qiskit.circuit.library.standard_gates.h.HGate,\n qiskit.circuit.library.standard_gates.x.CXGate]\n\n\nSet 20 sample steps and use rescaled guidance-formula.\n\npipeline.guidance_sample_mode = \"rescaled\"\npipeline.scheduler.set_timesteps(20)" + }, + { + "objectID": "examples/hello_circuit.html#inference-sampling", + "href": "examples/hello_circuit.html#inference-sampling", + "title": "Generate a circuit", + "section": "Inference / sampling", + "text": "Inference / sampling\nSet our desired condition SRV\n\nsrv = [2, 1, 2, 1, 2] # set your target SRV; can be 3 to 8 qubit\nnum_of_qubits = len(srv) \n\nprompt = f\"Generate SRV: {srv}\" # model was trained with this phrase\nprompt\n\n'Generate SRV: [2, 1, 2, 1, 2]'\n\n\nDefine sample parameters\n\ng = 7.5 # guidance scale\nmax_gates = 16 # how many time steps the tensor encoding has\nsamples = 32 # how many circuits to generate\n\nSample tokenized circuits\n\nout_tensor = infer_srv.generate_srv_tensors(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, g, no_bar=False)\n\n\n\n\n[INFO]: (generate_srv_tensors) Generated 32 tensors\n\n\nCheck how many distinct tensors we got:\n\nout_tensor.unique(dim=0).shape[0]\n\n32\n\n\nLet’s look what is generated. Note, 3 is the padding token (or empty action).\n\nout_tensor[:2]\n\ntensor([[[ 0, 2, 1, 2, -2, 0, 2, 0, 0, 2, 3, 3, 3, 3, 3, 3],\n [-2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3],\n [ 2, -2, 0, -2, 2, 1, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3],\n [ 0, 0, 0, 0, 0, 0, -2, 1, 0, 0, 3, 3, 3, 3, 3, 3],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 1, -2, 3, 3, 3, 3, 3, 3]],\n\n [[ 1, -2, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [ 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [ 0, 2, 0, -2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [ 0, 0, -2, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [ 0, 0, 2, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]])" + }, + { + "objectID": "examples/hello_circuit.html#convert-to-quiskit-circuit", + "href": "examples/hello_circuit.html#convert-to-quiskit-circuit", + "title": "Generate a circuit", + "section": "Convert to quiskit circuit", + "text": "Convert to quiskit circuit\nTo get a quiskit circuit we need to do:\n\napply cos. sim. to go from embeddings to token matrices (the function infer_srv.generate_srv_tensors did this already)\nparse token matrix to quiskit and filter out error circuits\ncalculate SRV and plot circuits\n\n\nqc_list, error_cnt, svr_list = infer_srv.convert_tensors_to_srvs(out_tensor, pipeline.gate_pool)\n\nGenerated error circuits:\n\nerror_cnt\n\n0\n\n\nWhat SRVs did we get:\n\nsvr_list[:4]\n\n[[2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2]]\n\n\nThat is an accuracy of:\n\nsum(srv==x for x in svr_list)/len(svr_list)\n\n0.9375\n\n\nFinally plot some of the circuits:\n\nfig, axs = plt.subplots(2, 4, figsize=(18,5), constrained_layout=True)\nfor qc,is_svr,ax in zip(qc_list, svr_list, axs.flatten()): \n qc.draw(\"mpl\", plot_barriers=False, ax=ax)\n ax.set_title(f\"{'Correct' if is_svr==srv else 'NOT correct'}, is SRV = {is_svr}\")\nplt.show()" + }, + { + "objectID": "metrics.html", + "href": "metrics.html", + "title": "Metrics", + "section": "", + "text": "Definition of metrics used during training.\n\nsource\n\nMetric\n\n Metric (name:str, device)\n\nBase metric class.\n\nsource\n\n\nMean\n\n Mean (name:str, device)\n\nMean metric, used for loss ..\n\nsource\n\n\nAccuracy\n\n Accuracy (name:str, device)\n\nAccuracy metric.\nExample usage:\n\na = Accuracy(\"mean\", \"cpu\")\nprint(a, a.empty)\n\na.update_state(torch.Tensor([3,2,2,1]), torch.Tensor([1,2,2,1]))\nprint(a, a.empty)\n\na.update_state(torch.Tensor([1,2,2,3]), torch.Tensor([1,2,2,3]))\nprint(a, a.empty)\n\na.reset_state()\nprint(a, a.empty)\n\nmean=nan True\nmean=0.75 False\nmean=0.875 False\nmean=nan True\n\n\n\n\n\n\n Back to top" + }, + { + "objectID": "platform/qcircuit_metrics.html", + "href": "platform/qcircuit_metrics.html", + "title": "Quantum circuit metrics", + "section": "", + "text": "Norms for unitary compilation." + }, + { + "objectID": "platform/qcircuit_metrics.html#unitary-distances", + "href": "platform/qcircuit_metrics.html#unitary-distances", + "title": "Quantum circuit metrics", + "section": "Unitary distances", + "text": "Unitary distances\n\nsource\n\nUnitary_FrobeniusNorm\n\n Unitary_FrobeniusNorm ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\na = torch.tensor([[1,2], [2, 1]]).float()\nb = torch.tensor([[2,3], [2, 2]]).float()\n\ng = Unitary_FrobeniusNorm\ng.distance(a,b)\n\ntensor(1.5000)" + }, + { + "objectID": "platform/simulation/qcircuit_sim.html", + "href": "platform/simulation/qcircuit_sim.html", + "title": "Qiskit: quantum circuit simulation", + "section": "", + "text": "source\n\n\n\n get_number_of_gate_params (gate_cls)\n\n\nsource\n\n\n\n\n gate_pool_to_gate_classes (gate_pool:list[qiskit.circuit.gate.Gate])\n\nCreates a vocabulary from a gate pool.\n\nsource\n\n\n\n\n schmidt_rank_vector\n (densityMatrix:qiskit.quantum_info.states.densitymat\n rix.DensityMatrix)\n\nReturn the SRV of a qi.DensityMatrix.\n\nsource\n\n\n\n\n rnd_circuit (num_of_qubits, num_of_gates,\n gate_pool:list[qiskit.circuit.gate.Gate], rng)\n\nCreate a random circuit.\n\nsource\n\n\n\n\n optimize_circuit (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,\n gate_pool:list[qiskit.circuit.gate.Gate],\n optimization_level=2)\n\nUse qiskit.compiler.transpile to optimize a circuit.\n\ngs = [ql.HGate, ql.CXGate, ql.CRXGate]\nqc = rnd_circuit(num_of_qubits=3, num_of_gates=8, gate_pool=gs, rng=np.random.default_rng())\nsvr = schmidt_rank_vector(qi.DensityMatrix(qc))\ndisplay(f\"Rand: svr={svr} num_gates={len(qc.data)}\", qc.draw('mpl'))\n\nqc = optimize_circuit(qc, gs)\nsvr = schmidt_rank_vector(qi.DensityMatrix(qc))\ndisplay(f\"Opti: svr={svr} num_gates={len(qc.data)}\", qc.draw('mpl'))\n\n'Rand: svr=[1, 1, 1] num_gates=8'\n\n\n\n\n\n'Opti: svr=[1, 1, 1] num_gates=8'\n\n\n\n\n\n\nsource\n\n\n\n\n plot_svr_stat (num_of_qubits, min_gates, max_gates, gs, samples,\n sort=False, opt=True, rng=Generator(PCG64) at\n 0x7F4E2117AC80)\n\nSRV distrubtion for random sampling\n\n# gs = [ql.HGate, ql.SGate, ql.TGate, ql.CXGate] # approx universal set\n# gs = [ql.HGate, ql.CCXGate] # approx universal set\ngs = [ql.HGate, ql.CXGate]\n\nplot_svr_stat(num_of_qubits=3, min_gates=6, max_gates=8, gs=gs, samples=int(1e3), rng=np.random.default_rng())\n\n[1, 1, 1]: 58.2%\n[2, 2, 1]: 11.5%\n[2, 2, 2]: 11.3%\n[1, 2, 2]: 9.6%\n[2, 1, 2]: 9.4%" + }, + { + "objectID": "platform/simulation/qcircuit_sim.html#circuit", + "href": "platform/simulation/qcircuit_sim.html#circuit", + "title": "Qiskit: quantum circuit simulation", + "section": "", + "text": "source\n\n\n\n get_number_of_gate_params (gate_cls)\n\n\nsource\n\n\n\n\n gate_pool_to_gate_classes (gate_pool:list[qiskit.circuit.gate.Gate])\n\nCreates a vocabulary from a gate pool.\n\nsource\n\n\n\n\n schmidt_rank_vector\n (densityMatrix:qiskit.quantum_info.states.densitymat\n rix.DensityMatrix)\n\nReturn the SRV of a qi.DensityMatrix.\n\nsource\n\n\n\n\n rnd_circuit (num_of_qubits, num_of_gates,\n gate_pool:list[qiskit.circuit.gate.Gate], rng)\n\nCreate a random circuit.\n\nsource\n\n\n\n\n optimize_circuit (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,\n gate_pool:list[qiskit.circuit.gate.Gate],\n optimization_level=2)\n\nUse qiskit.compiler.transpile to optimize a circuit.\n\ngs = [ql.HGate, ql.CXGate, ql.CRXGate]\nqc = rnd_circuit(num_of_qubits=3, num_of_gates=8, gate_pool=gs, rng=np.random.default_rng())\nsvr = schmidt_rank_vector(qi.DensityMatrix(qc))\ndisplay(f\"Rand: svr={svr} num_gates={len(qc.data)}\", qc.draw('mpl'))\n\nqc = optimize_circuit(qc, gs)\nsvr = schmidt_rank_vector(qi.DensityMatrix(qc))\ndisplay(f\"Opti: svr={svr} num_gates={len(qc.data)}\", qc.draw('mpl'))\n\n'Rand: svr=[1, 1, 1] num_gates=8'\n\n\n\n\n\n'Opti: svr=[1, 1, 1] num_gates=8'\n\n\n\n\n\n\nsource\n\n\n\n\n plot_svr_stat (num_of_qubits, min_gates, max_gates, gs, samples,\n sort=False, opt=True, rng=Generator(PCG64) at\n 0x7F4E2117AC80)\n\nSRV distrubtion for random sampling\n\n# gs = [ql.HGate, ql.SGate, ql.TGate, ql.CXGate] # approx universal set\n# gs = [ql.HGate, ql.CCXGate] # approx universal set\ngs = [ql.HGate, ql.CXGate]\n\nplot_svr_stat(num_of_qubits=3, min_gates=6, max_gates=8, gs=gs, samples=int(1e3), rng=np.random.default_rng())\n\n[1, 1, 1]: 58.2%\n[2, 2, 1]: 11.5%\n[2, 2, 2]: 11.3%\n[1, 2, 2]: 9.6%\n[2, 1, 2]: 9.4%" + }, + { + "objectID": "platform/simulation/qcircuit_sim.html#svr-test-cases", + "href": "platform/simulation/qcircuit_sim.html#svr-test-cases", + "title": "Qiskit: quantum circuit simulation", + "section": "SVR Test cases", + "text": "SVR Test cases\n\ndef test_srv(system_dims, init, target):\n vec = qi.Statevector(init, dims=system_dims)\n vec *= 1/np.sqrt(vec.trace())\n srv = schmidt_rank_vector(qi.DensityMatrix(vec)) \n assert srv == target, f\"srv: {srv}\"\n print(f\"passed test, svr: {srv}\")\n display(vec.draw('latex', prefix='|\\\\psi\\\\rangle = '))\n\n\n#---------------- |0+> = |00>+|01>\nsystem_dims = (2,2)\ninit = np.zeros(np.prod(system_dims), dtype=complex)\ninit[0] = 1\ninit[1] = 1\ntest_srv(system_dims, init, [1, 1])\n\n#----------------Bell, |00>+|11>\nsystem_dims = (2,2)\ninit = np.zeros(np.prod(system_dims), dtype=complex)\ninit[0] = 1\ninit[3] = 1\ntest_srv(system_dims, init, [2, 2])\n \n#----------------GHZ, |000>+|111>\nsystem_dims = (2,2,2)\ninit = np.zeros(np.prod(system_dims), dtype=complex)\ninit[0] = 1\ninit[7] = 1\ntest_srv(system_dims, init, [2,2,2])\n \n#----------------Sym, |000>+|111>+|222>\nsystem_dims = (3,3,3)\ninit = np.zeros(np.prod(system_dims), dtype=complex)\ninit[0] = 1\ninit[13] = 1\ninit[26] = 1\ntest_srv(system_dims, init, [3,3,3])\n \n#----------------Wikipedia example, |000>+|101>+|210>+|311>\nsystem_dims = (4,4,4)\ninit = np.zeros(np.prod(system_dims), dtype=complex)\ninit[0] = 1\ninit[17] = 1\ninit[36] = 1\ninit[53] = 1\ntest_srv(system_dims, init, [2, 2, 4])\n\npassed test, svr: [1, 1]\npassed test, svr: [2, 2]\npassed test, svr: [2, 2, 2]\npassed test, svr: [3, 3, 3]\npassed test, svr: [2, 2, 4]\n\n\n\\[|\\psi\\rangle = \\frac{\\sqrt{2}}{2} |00\\rangle+\\frac{\\sqrt{2}}{2} |01\\rangle\\]\n\n\n\\[|\\psi\\rangle = \\frac{\\sqrt{2}}{2} |00\\rangle+\\frac{\\sqrt{2}}{2} |11\\rangle\\]\n\n\n\\[|\\psi\\rangle = \\frac{\\sqrt{2}}{2} |000\\rangle+\\frac{\\sqrt{2}}{2} |111\\rangle\\]\n\n\n$$\\[\\begin{align}\n\n|\\psi\\rangle =\n\\begin{bmatrix}\n\\frac{\\sqrt{3}}{3} & 0 & 0 & 0 & \\cdots & 0 & 0 & \\frac{\\sqrt{3}}{3} \\\\\n\\end{bmatrix}\n\\\\\n\\text{dims=(3, 3, 3)}\n\\end{align}\\]$$\n\n\n$$\\[\\begin{align}\n\n|\\psi\\rangle =\n\\begin{bmatrix}\n\\frac{1}{2} & 0 & 0 & 0 & \\cdots & 0 & 0 & 0 \\\\\n\\end{bmatrix}\n\\\\\n\\text{dims=(4, 4, 4)}\n\\end{align}\\]$$" + }, + { + "objectID": "platform/qcircuit_evaluation.html", + "href": "platform/qcircuit_evaluation.html", + "title": "Quantum circuit evaluation", + "section": "", + "text": "source\n\n\n\n sort_into_bins (x, y, y_uniques)\n\n\nsource\n\n\n\n\n extract_gate_number (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,\n gate_pool, max_gates)\n\n\nsource\n\n\n\n\n get_gate_stat_from_tensors (tensors, gate_pool)\n\n\nsource\n\n\n\n\n get_gate_stat_from_circuits (qcs:list, gate_pool, max_gates)" + }, + { + "objectID": "platform/qcircuit_evaluation.html#gate-count", + "href": "platform/qcircuit_evaluation.html#gate-count", + "title": "Quantum circuit evaluation", + "section": "", + "text": "source\n\n\n\n sort_into_bins (x, y, y_uniques)\n\n\nsource\n\n\n\n\n extract_gate_number (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,\n gate_pool, max_gates)\n\n\nsource\n\n\n\n\n get_gate_stat_from_tensors (tensors, gate_pool)\n\n\nsource\n\n\n\n\n get_gate_stat_from_circuits (qcs:list, gate_pool, max_gates)" + }, + { + "objectID": "platform/qcircuit_util.html", + "href": "platform/qcircuit_util.html", + "title": "Quantum circuit utils", + "section": "", + "text": "source\n\n\n\n get_element_matching_indices (a, b)\n\nCompares (2d) a with b. Returns the indices of b, where a element of a matches with b.\n\nsource\n\n\n\n\n get_entanglement_bins (num_of_qubits)\n\nReturns all SRV sorted in entangle bins which correspond to a number of entangled qubits.\n\nfor srvs,label in zip(*get_entanglement_bins(5)):\n print(label, \":\", srvs)\n\n0 qubit entangled : [[1, 1, 1, 1, 1]]\n2 qubit entangled : [[1, 1, 1, 2, 2], [1, 1, 2, 1, 2], [1, 1, 2, 2, 1], [1, 2, 1, 1, 2], [1, 2, 1, 2, 1], [1, 2, 2, 1, 1], [2, 1, 1, 1, 2], [2, 1, 1, 2, 1], [2, 1, 2, 1, 1], [2, 2, 1, 1, 1]]\n3 qubit entangled : [[1, 1, 2, 2, 2], [1, 2, 1, 2, 2], [1, 2, 2, 1, 2], [1, 2, 2, 2, 1], [2, 1, 1, 2, 2], [2, 1, 2, 1, 2], [2, 1, 2, 2, 1], [2, 2, 1, 1, 2], [2, 2, 1, 2, 1], [2, 2, 2, 1, 1]]\n4 qubit entangled : [[1, 2, 2, 2, 2], [2, 1, 2, 2, 2], [2, 2, 1, 2, 2], [2, 2, 2, 1, 2], [2, 2, 2, 2, 1]]\n5 qubit entangled : [[2, 2, 2, 2, 2]]" + }, + { + "objectID": "platform/qcircuit_util.html#srv", + "href": "platform/qcircuit_util.html#srv", + "title": "Quantum circuit utils", + "section": "", + "text": "source\n\n\n\n get_element_matching_indices (a, b)\n\nCompares (2d) a with b. Returns the indices of b, where a element of a matches with b.\n\nsource\n\n\n\n\n get_entanglement_bins (num_of_qubits)\n\nReturns all SRV sorted in entangle bins which correspond to a number of entangled qubits.\n\nfor srvs,label in zip(*get_entanglement_bins(5)):\n print(label, \":\", srvs)\n\n0 qubit entangled : [[1, 1, 1, 1, 1]]\n2 qubit entangled : [[1, 1, 1, 2, 2], [1, 1, 2, 1, 2], [1, 1, 2, 2, 1], [1, 2, 1, 1, 2], [1, 2, 1, 2, 1], [1, 2, 2, 1, 1], [2, 1, 1, 1, 2], [2, 1, 1, 2, 1], [2, 1, 2, 1, 1], [2, 2, 1, 1, 1]]\n3 qubit entangled : [[1, 1, 2, 2, 2], [1, 2, 1, 2, 2], [1, 2, 2, 1, 2], [1, 2, 2, 2, 1], [2, 1, 1, 2, 2], [2, 1, 2, 1, 2], [2, 1, 2, 2, 1], [2, 2, 1, 1, 2], [2, 2, 1, 2, 1], [2, 2, 2, 1, 1]]\n4 qubit entangled : [[1, 2, 2, 2, 2], [2, 1, 2, 2, 2], [2, 2, 1, 2, 2], [2, 2, 2, 1, 2], [2, 2, 2, 2, 1]]\n5 qubit entangled : [[2, 2, 2, 2, 2]]" + }, + { + "objectID": "platform/qcircuit_dataset_construction.html", + "href": "platform/qcircuit_dataset_construction.html", + "title": "Quantum circuit dataset construction", + "section": "", + "text": "Functions to construct a dataset. Here we define the tokenization (encoding and decoding)." + }, + { + "objectID": "platform/qcircuit_dataset_construction.html#tokenizer-encoding-and-decoding", + "href": "platform/qcircuit_dataset_construction.html#tokenizer-encoding-and-decoding", + "title": "Quantum circuit dataset construction", + "section": "Tokenizer: encoding and decoding", + "text": "Tokenizer: encoding and decoding\n\nsource\n\nget_target_control_qubits\n\n get_target_control_qubits\n (qc:qiskit.circuit.quantumcircuit.QuantumCircu\n it, gate:qiskit.circuit.gate.Gate)\n\n\nsource\n\n\nencode_circuit\n\n encode_circuit (qc:qiskit.circuit.quantumcircuit.QuantumCircuit,\n num_of_qubits, gate_classes:dict, max_gates:int,\n sign_labels={'control_qubits': -1, 'target_qubits': 1},\n return_params=False)\n\n\nsource\n\n\ndecode_circuit\n\n decode_circuit (enc_tensor:torch.Tensor,\n gate_pool:list[qiskit.circuit.gate.Gate],\n place_barrier=True, sign_labels={'control_qubits': -1,\n 'target_qubits': 1}, params_tensor=None)" + }, + { + "objectID": "platform/qcircuit_dataset_construction.html#dataset-generation", + "href": "platform/qcircuit_dataset_construction.html#dataset-generation", + "title": "Quantum circuit dataset construction", + "section": "Dataset generation", + "text": "Dataset generation\n\nTotally random SRV circuits\n\nsource\n\n\nget_rnd_encoded_circuit\n\n get_rnd_encoded_circuit (num_of_qubits, min_gates, max_gates, gate_pool,\n gate_classes, rng, optimized=True,\n return_params=False)\n\n\nsource\n\n\nget_rnd_encoded_circuits\n\n get_rnd_encoded_circuits (samples, num_of_qubits=3, min_gates=3,\n max_gates=10, gate_pool=[<class 'qiskit.circuit\n .library.standard_gates.h.HGate'>, <class 'qisk\n it.circuit.library.standard_gates.x.CXGate'>],\n optimized=True, silent=False,\n return_params=False)\n\n\ngate_pool=[ql.HGate, ql.CXGate, ql.CU3Gate, ql.CRXGate]\n\nprint(\"Encode:\")\nenc_t, y, params = get_rnd_encoded_circuits(samples=1, num_of_qubits=3, min_gates=6, max_gates=6, gate_pool=gate_pool, optimized=True, return_params=True)\n\nfor enc_i, y_i, params_i in zip(enc_t, y, params):\n print(f\"{enc_i=}\")\n print(f\"{y_i=}\")\n print(f\"{params_i=}\")\n\nprint(\"Decode:\")\nqc = decode_circuit(enc_t[0], gate_pool=gate_pool, params_tensor=params[0])\ndisplay(qc.draw(\"mpl\"))\n\nEncode:\nenc_i=tensor([[-2, 0, -2, 3, 3, 0],\n [ 2, 1, 2, -3, -3, 0],\n [ 0, 0, 0, 0, 0, 1]], dtype=torch.int32)\ny_i=[2, 2, 1]\nparams_i=tensor([[0.0000, 0.0000, 0.0000, 2.2710, 4.8585, 0.0000],\n [0.0000, 0.0000, 0.0000, 0.0304, 1.1044, 0.0000],\n [0.0000, 0.0000, 0.0000, 3.0486, 4.9504, 0.0000]])\nDecode:\n\n\n\n\n\n\n\n\n\nsource\n\n\ngen_qc_dataset\n\n gen_qc_dataset (samples, num_of_qubits, min_gates, max_gates, gate_pool,\n optimized, silent=False)\n\n\n\nSpecific random SRV circuit\n\nsource\n\n\nget_specific_rnd_srv_circuit\n\n get_specific_rnd_srv_circuit (srv, requested_length, gate_pool,\n max_i=2000, silent=True,\n fix_length_after_optimizing=True,\n requested_length_tolerance=0)\n\n\n\nUnitary dataset\n\nsource\n\n\ngen_compilation_rndGates_dataset\n\n gen_compilation_rndGates_dataset (samples, num_of_qubits, min_gates,\n max_gates, gate_pool,\n min_sub_gate_pool_cnt=1, silent=False)\n\nSamples rnd circuit with a rnd subset of gates and return qc with gate label and unitary\n\ngate_pool=[ql.HGate, ql.CXGate, ql.ZGate, ql.XGate, ql.CCXGate]\n\nenc_t, y, U = gen_compilation_rndGates_dataset(samples=1, num_of_qubits=3, min_gates=3, max_gates=4, gate_pool=gate_pool)\n\nnp.set_printoptions(edgeitems=30, linewidth=100000, formatter=dict(float=lambda x: \"%.3g\" % x))\n\nprint(f\"\\ny Label >>> {y[0]} <<<\")\nprint(f\"\\n{enc_t[0]}\")\nprint(f\"\\n{U[0]}\")\nprint(\"\\nDecoded:\")\nqc = decode_circuit(enc_t[0], gate_pool=gate_pool)\ndisplay(qc.draw(\"mpl\"))\n\n\n\n\ngenerated unique circuits: 1\n\ny Label >>> Compile using: ['x'] <<<\n\ntensor([[0, 0, 0, 0],\n [4, 0, 0, 0],\n [0, 4, 4, 4]], dtype=torch.int32)\n\n[[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 1.+0.j]\n [0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+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 1.+0.j 0.+0.j 0.+0.j]\n [0.+0.j 0.+0.j 1.+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 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]\n [1.+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 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]]\n\nDecoded:\n\n\n\n\n\n\n\nGraph states dataset\n\n#place all h on all bist then only cz" + }, + { + "objectID": "examples/editing_and_masking.html", + "href": "examples/editing_and_masking.html", + "title": "Editing and masking of circuits", + "section": "", + "text": "In this notebook we show editing and masking of circuits.\nfrom genQC.imports import *\nfrom genQC.pipeline.diffusion_pipeline import DiffusionPipeline\nfrom genQC.inference.infer_srv import convert_tensors_to_srvs, schmidt_rank_vector\nimport genQC.platform.qcircuit_dataset_construction as data_const\nimport genQC.util as util\nfrom qiskit.quantum_info import DensityMatrix\ndevice = util.infer_torch_device() # use cuda if we can\nutil.MemoryCleaner.purge_mem() # clean existing memory alloc\n\n[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul." + }, + { + "objectID": "examples/editing_and_masking.html#setup-and-load", + "href": "examples/editing_and_masking.html#setup-and-load", + "title": "Editing and masking of circuits", + "section": "Setup and load", + "text": "Setup and load\nLoad the pre-trained model\n\nmodel_path = \"../../saves/qc_unet_config_SRV_3to8_qubit/\"\npipeline = DiffusionPipeline.from_config_file(model_path, device)\n\n[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.\n\n\nSet 20 sample steps and use rescaled guidance-formula.\n\npipeline.guidance_sample_mode = \"rescaled\"\npipeline.scheduler.set_timesteps(40) \ng = 7.5" + }, + { + "objectID": "examples/editing_and_masking.html#editing", + "href": "examples/editing_and_masking.html#editing", + "title": "Editing and masking of circuits", + "section": "1. Editing", + "text": "1. Editing\nSample a random circuit with desired parameters as the circuit we want to edit:\n\nsrv_init = [1, 1, 1, 2, 2] # psi_0 state\ndesired_length = 5 # 5 gates initially placed\n\n\ninit_qc = data_const.get_specific_rnd_srv_circuit(srv_init, desired_length, pipeline.gate_pool)\nprint(\"SRV is\", schmidt_rank_vector(DensityMatrix(init_qc)))\ninit_qc.draw(\"mpl\")\n\nSRV is [1, 1, 1, 2, 2]\n\n\n\n\n\nThe editing taks is analogous to image editing, we do img2img with conditioning and copy non-edit areas at every time step. Also called latent_filling.\n\ndef create_edited_circuits(pipeline, samples, qc, prompt, new_length, num_of_qubits, system_size, t_start_index):\n #-------------------------------------------\n # set mask - appending mask!\n old_length = len(qc.data)\n\n qubit_mask = torch.ones((system_size, new_length), device=device)\n qubit_mask[:, :old_length] = 0\n \n #-------------------------------------------\n # prepare and encode\n \n gate_classes = data_const.gate_pool_to_gate_classes(pipeline.gate_pool)\n \n emb_org_image = data_const.encode_circuit(qc, system_size, gate_classes, new_length).unsqueeze(0).to(device)\n emb_org_image = pipeline.model.embedd_clrs(emb_org_image)\n\n emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))\n \n #-------------------------------------------\n # prep condition\n \n c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))\n c = c.repeat(samples, *[1]*(c.dim()-1))\n\n #-------------------------------------------\n # latent fill\n out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)\n out_tensor = pipeline.model.invert_clr(out_tensor)\n out_tensor = out_tensor[:, :num_of_qubits]\n out_tensor = torch.unique(out_tensor, dim=0) # we only are interested in unique circuits\n \n qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)\n\n return qc_list, srv_list\n\n\nsamples = 16 # how many circuits we sample\nnew_length = 16 # how many gates the model can place \n\nsrv_target = [2, 2, 2, 2, 2] # desired target SRV\n\nnum_of_qubits = len(srv_target)\nt_start_index = t_start_index = int(0.05 * pipeline.scheduler.timesteps.shape[0]) # time step index at which we start denoising\n\nprompt = f\"Generate SRV: {srv_target}\" # model was trained with this phrase\nprompt\n\n'Generate SRV: [2, 2, 2, 2, 2]'\n\n\n\n# returns only distinct circuits\nedited_qc, srv_list = create_edited_circuits(pipeline, samples, init_qc, prompt, new_length, num_of_qubits, num_of_qubits, t_start_index)\n\n\n\n\nPick only correct ones:\n\ncorrect_edited_qc = []\nfor qc,srv in zip(edited_qc, srv_list):\n if srv==srv_target: correct_edited_qc.append(qc)\nprint(f\"We found {len(correct_edited_qc)} correct distinct solutions.\")\n\nWe found 12 correct distinct solutions.\n\n\nCompare: initial circuit\n\ninit_qc.draw(\"mpl\")\n\n\n\n\nv.s. edited:\n\nprint(\"SRV is\", schmidt_rank_vector(DensityMatrix(correct_edited_qc[0])))\ncorrect_edited_qc[0].draw(\"mpl\", plot_barriers=False)\n\nSRV is [2, 2, 2, 2, 2]\n\n\n\n\n\n\nfig, axs = plt.subplots(2,4, figsize=(18,5), constrained_layout=True)\nfor qc,ax in zip(correct_edited_qc, axs.flatten()): \n qc.draw(\"mpl\", plot_barriers=False, ax=ax)\nplt.show()" + }, + { + "objectID": "examples/editing_and_masking.html#masking", + "href": "examples/editing_and_masking.html#masking", + "title": "Editing and masking of circuits", + "section": "2. Masking", + "text": "2. Masking\nFirst we set a desired mask, i.e. a specific layout of a quantum processor.\n\nmax_gates = 16\nnum_of_qubits = 5\n\nd = 3\n#------\ndef con_set(q1, q2, x, d): \n qubit_mask[q1, x:x+d] = 1\n qubit_mask[q2, x:x+d] = 1\n return x+d\n\n#------\nx = 0\n\nqubit_mask = torch.zeros((num_of_qubits, max_gates), device=device) # mask: ones are getting filled, zeros are fixed !\nx = con_set(0, 1, x, d)\nx = con_set(1, 2, x, d)\nx = con_set(1, 3, x, d)\nx = con_set(3, 4, x, d)\n\n\ndef plot_mask():\n fig = plt.figure(figsize=(3.7,2), constrained_layout=True)\n plt.imshow(qubit_mask.cpu(), cmap=\"Greens\")\n plt.xticks(range(0, qubit_mask.shape[1], 2),fontsize=9)\n plt.yticks(range(num_of_qubits), fontsize=9)\n plt.xlabel(\"Gate sequence / time\", fontsize=12)\n plt.ylabel(\"Qubits\", fontsize=12)\n plt.show()\nplot_mask()\n\n\n\n\n\ndef get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask):\n org_image = torch.zeros((1, system_size, max_gates), device=device, dtype=torch.int32) \n \n padd_tok = len(pipeline.gate_pool) + 1\n padd_pos = (torch.ceil(torch.tensor(target_num_gates) / 4) * 4).to(torch.int32)\n org_image[:, :, padd_pos:] = padd_tok\n org_image[:, target_num_bits:, ] = padd_tok\n\n emb_org_image = pipeline.model.embedd_clrs(org_image)\n emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))\n \n return emb_org_images\n\n\ndef generate_pattern_SRV(pipeline, prompt, samples, system_size, num_of_qubits, max_gates, qubit_mask, t_start_index=0, target_num_gates=None, target_num_bits=None): \n\n if not exists(target_num_gates):\n target_num_gates = max_gates\n\n if not exists(target_num_bits):\n target_num_bits = num_of_qubits\n \n emb_org_images = get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask)\n\n #----------------\n # prep condition\n\n c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))\n c = c.repeat(samples, *[1]*(c.dim()-1))\n\n #----------------\n # latent fill\n \n out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)\n out_tensor = pipeline.model.invert_clr(out_tensor)\n out_tensor = out_tensor[:, :num_of_qubits]\n out_tensor = torch.unique(out_tensor, dim=0)\n \n qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)\n\n return qc_list, srv_list\n\nNow generate circuits corresponding to the mask.\n\nsamples = 512 # how many circuits we sample\nsrv_target = [2, 1, 2, 2, 2] # desired target SRV\n\nassert len(srv_target)==qubit_mask.shape[0]\n\nprompt = f\"Generate SRV: {srv_target}\" # model was trained with this phrase\nprompt\n\n'Generate SRV: [2, 1, 2, 2, 2]'\n\n\n\nqc_list, srv_list = generate_pattern_SRV(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, qubit_mask, t_start_index=1)\n\n\n\n\nPick only correct ones:\n\ncorrect_qc = []\nfor qc,srv in zip(qc_list, srv_list):\n if srv==srv_target: correct_qc.append(qc)\nprint(f\"We found {len(correct_qc)} correct distinct solutions.\")\n\nWe found 9 correct distinct solutions.\n\n\nLet’s plot them. Mask:\n\nplot_mask()\n\n\n\n\nv.s. solution:\n\nprint(\"SRV is\", schmidt_rank_vector(DensityMatrix(correct_qc[0])))\ncorrect_qc[0].draw(\"mpl\", plot_barriers=False)\n\nSRV is [2, 1, 2, 2, 2]\n\n\n\n\n\n\nfig, axs = plt.subplots(1, min(len(correct_qc), 4), figsize=(18,5), constrained_layout=True)\nfor qc,ax in zip(correct_qc, axs.flatten()): \n qc.draw(\"mpl\", plot_barriers=False, ax=ax)\nplt.show()" + }, + { + "objectID": "examples/unitary_compilation.html", + "href": "examples/unitary_compilation.html", + "title": "Compile unitaries", + "section": "", + "text": "In this notebook we want use the unitary compilation model.\nfrom genQC.imports import *\nfrom genQC.pipeline.diffusion_pipeline import DiffusionPipeline\nfrom qiskit import QuantumCircuit\nfrom genQC.inference.infer_compilation import generate_comp_tensors, get_gate_and_U_acc\nfrom genQC.printing import display_colums\nimport genQC.platform.qcircuit_dataset_construction as data_const\nimport qiskit.quantum_info as qi\nimport genQC.util as util\nimport ast\ndevice = util.infer_torch_device() # use cuda if we can\nutil.MemoryCleaner.purge_mem() # clean existing memory alloc\n\n[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\ndef str_cond_to_gate_indices(y): # helper function, used to check if only allowed gates were used by the model!\n assert y[:15] == \"Compile using: \"\n c = ast.literal_eval(y[15:]) \n gate_classes = data_const.gate_pool_to_gate_classes(pipeline.gate_pool)\n gate_clrs = [0] + [gate_classes[ic] for ic in c] # 0 is empty, always allowed!\n return gate_clrs" + }, + { + "objectID": "examples/unitary_compilation.html#setup-and-load", + "href": "examples/unitary_compilation.html#setup-and-load", + "title": "Compile unitaries", + "section": "Setup and load", + "text": "Setup and load\nLoad the pre-trained model\n\nmodel_path = \"../../saves/qc_unet_config_Compilation_3_qubit/\"\npipeline = DiffusionPipeline.from_config_file(model_path, device)\n\n[INFO]: `genQC.models.unet_qc.QC_Compilation_UNet` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.\n[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.\n\n\nSet 20 sample steps and use rescaled guidance-formula.\n\npipeline.guidance_sample_mode = \"rescaled\"\npipeline.scheduler.set_timesteps(20) \ng = 7.5\n\nThe model was trained with a gate pool of:\n\npipeline.gate_pool\n\n[qiskit.circuit.library.standard_gates.h.HGate,\n qiskit.circuit.library.standard_gates.x.CXGate,\n qiskit.circuit.library.standard_gates.z.ZGate,\n qiskit.circuit.library.standard_gates.x.XGate,\n qiskit.circuit.library.standard_gates.x.CCXGate,\n qiskit.circuit.library.standard_gates.swap.SwapGate]" + }, + { + "objectID": "examples/unitary_compilation.html#compile-a-unitary", + "href": "examples/unitary_compilation.html#compile-a-unitary", + "title": "Compile unitaries", + "section": "Compile a unitary", + "text": "Compile a unitary\nCompile a given unitary \\(U\\). Note, there has to be a solution with the pipeline.gate_pool in order to find the exact solution.\n\ndef compile_and_plot(U, prompt):\n U_r, U_i = torch.Tensor(np.real(U)), torch.Tensor(np.imag(U))\n U_tensor = torch.stack([U_r, U_i], dim=0)\n \n out_tensor = generate_comp_tensors(pipeline, prompt, U_tensor, samples, num_of_qubits, num_of_qubits, max_gates, g, unique=True)\n _, _, _, _, _, comb_corr_qc, _, _, _ = get_gate_and_U_acc(out_tensor, str_cond_to_gate_indices(prompt), U_tensor, pipeline.gate_pool, num_of_qubits, max_gates)\n comb_corr_qc = sorted(comb_corr_qc, key=lambda x: len(x.data)) #sort to get the shortest solutions\n\n fig, axs = plt.subplots(1,4, figsize=(18,5), constrained_layout=True)\n axs[0].set_title(f\"{prompt}\")\n for qc,ax in zip(comb_corr_qc, axs.flatten()): \n qc.draw(\"mpl\", plot_barriers=False, ax=ax)\n plt.show()\n\n\nsamples = 512\nnum_of_qubits = 3\nmax_gates = 12\n\n\nprompt = \"Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']\" # model was trained with phrases like this, allow full gate set\nprompt\n\n\"Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']\"\n\n\n\nExercise 1\nInspired from (quantumcomputing.stackexchange.com/questions/13821/generate-a-3-qubit-swap-unitary-in-terms-of-elementary-gates/13826). Note, this unitary WAS in the training set.\n\nU = np.matrix([[1,0,0,0,0,0,0,0],\n [0,1,0,0,0,0,0,0],\n [0,0,1,0,0,0,0,0],\n [0,0,0,0,1,0,0,0],\n [0,0,0,1,0,0,0,0],\n [0,0,0,0,0,1,0,0],\n [0,0,0,0,0,0,1,0],\n [0,0,0,0,0,0,0,1]], dtype=np.complex128) \n\nassert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary\n\nPlot correct (exact) compiled circuits:\n\ncompile_and_plot(U, prompt)\n\n\n\n\n\n\nExercise 2\nInspired from (quantumcomputing.stackexchange.com/questions/12439/procedures-and-intuition-for-designing-simple-quantum-circuits/12440). Note, this unitary WAS in the training set.\n\nU = np.matrix([[1,0,0,0,0,0,0,0],\n [0,0,0,0,0,0,0,1],\n [0,1,0,0,0,0,0,0],\n [0,0,1,0,0,0,0,0],\n [0,0,0,1,0,0,0,0],\n [0,0,0,0,1,0,0,0],\n [0,0,0,0,0,1,0,0],\n [0,0,0,0,0,0,1,0]], dtype=np.complex128) \n\nassert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary\n\nPlot correct (exact) compiled circuits:\n\ncompile_and_plot(U, prompt)\n\n\n\n\n\n\nExercise 3\nA randomly generated unitary (from a random circuit). This unitary WAS NOT in the training set, it is new to the model!\n\nU = np.matrix([[ 0.70710678, 0. , 0. , 0. , 0.70710678, 0. , 0. , 0. ],\n [ 0. , -0.70710678, 0. , 0. , 0. , -0.70710678, 0. , 0. ],\n [-0.70710678, 0. , 0. , 0. , 0.70710678, 0. , 0. , 0. ],\n [ 0. , 0.70710678, 0. , 0. , 0. , -0.70710678, 0. , 0. ],\n [ 0. , 0. , 0.70710678, 0. , 0. , 0. , 0. , 0.70710678],\n [ 0. , 0. , 0. , 0.70710678, 0. , 0. , 0.70710678, 0. ],\n [ 0. , 0. , -0.70710678, 0. , 0. , 0. , 0. , 0.70710678],\n [ 0. , 0. , 0. ,-0.70710678, 0. , 0. , 0.70710678, 0. ]], dtype=np.complex128)\n\nassert np.allclose(U.H@U, np.identity(2**num_of_qubits)) and np.allclose(U@U.H, np.identity(2**num_of_qubits)) #check if unitary\n\nPlot correct (exact) compiled circuits:\n\ncompile_and_plot(U, prompt)" + }, + { + "objectID": "examples/unitary_compilation.html#transpile-and-discover", + "href": "examples/unitary_compilation.html#transpile-and-discover", + "title": "Compile unitaries", + "section": "Transpile and discover", + "text": "Transpile and discover\nSet an initial circuit we want to transpile, optimize or use for discovering sub-arrangements:\n\nqc = QuantumCircuit(3)\nqc.h(2)\nqc.cx(0,1)\nqc.cx(2,1)\nqc.h(1)\nqc.x(1)\nqc.h(1)\nqc.x(2)\n\nU = qi.Operator(qc).to_matrix() # the unitary of the circuit\n\n#-----------------------------------------\n\nfig = qc.draw(\"mpl\")\nfig\n\n\n\n\nWe set different gate pool targets to see what the model gives us:\n\ncs_1 = f\"Compile using: {[x().name for x in pipeline.gate_pool]}\", \"all\"\n\ncs_2 = \"Compile using: ['h', 'cx', 'z', 'ccx']\" , \"no x, no swap\" \ncs_3 = \"Compile using: ['h', 'cx', 'x', 'ccx']\" , \"no z, no swap\" \ncs_4 = \"Compile using: ['h', 'x', 'ccx']\" , \"no cx, no z, no swap\" \ncs_5 = \"Compile using: ['h', 'z', 'x', 'ccx']\" , \"no cx, no swap\" \n\ncs = [cs_1, cs_2, cs_3, cs_4, cs_5]\ncs\n\n[(\"Compile using: ['h', 'cx', 'z', 'x', 'ccx', 'swap']\", 'all'),\n (\"Compile using: ['h', 'cx', 'z', 'ccx']\", 'no x, no swap'),\n (\"Compile using: ['h', 'cx', 'x', 'ccx']\", 'no z, no swap'),\n (\"Compile using: ['h', 'x', 'ccx']\", 'no cx, no z, no swap'),\n (\"Compile using: ['h', 'z', 'x', 'ccx']\", 'no cx, no swap')]\n\n\n\nsamples = 512\nnum_of_qubits = 3\nmax_gates = 12\n\nCompile with the different gate-sets and plot correct (exact) compiled circuits. Note, some of the circuits might look the same but the gate time-sequences are distinct. Qiskit reorders “parallel” gates to make smaller plots.\n\nfor c, note in cs: compile_and_plot(U, c)" + }, + { + "objectID": "pipeline/diffusion_pipeline.html", + "href": "pipeline/diffusion_pipeline.html", + "title": "Diffusion Pipeline", + "section": "", + "text": "source\n\nDiffusionPipeline\n\n DiffusionPipeline (scheduler:genQC.scheduler.scheduler.Scheduler,\n model:torch.nn.modules.module.Module,\n text_encoder:torch.nn.modules.module.Module,\n device:torch.device, enable_guidance_train=True,\n guidance_train_p=0.1, cached_text_enc=True)\n\nA Pipeline for diffusion models. Implements train and inference functions. Diffusion parameters are defined inside a Scheduler object.\n\n\n\n\n Back to top" + }, + { + "objectID": "pipeline/diffusion_pipeline_special.html", + "href": "pipeline/diffusion_pipeline_special.html", + "title": "Diffusion Pipeline Special", + "section": "", + "text": "source\n\nDiffusionPipeline_attnPadded\n\n DiffusionPipeline_attnPadded\n (scheduler:genQC.scheduler.scheduler.Schedu\n ler, model:torch.nn.modules.module.Module, \n text_encoder:torch.nn.modules.module.Module\n , device:torch.device,\n enable_guidance_train=True,\n guidance_train_p=0.1, cached_text_enc=True)\n\nA special DiffusionPipeline with attention masking.\n\nsource\n\n\nDiffusionPipeline_Compilation\n\n DiffusionPipeline_Compilation\n (scheduler:genQC.scheduler.scheduler.Sched\n uler,\n model:torch.nn.modules.module.Module, text\n _encoder:torch.nn.modules.module.Module,\n device:torch.device,\n enable_guidance_train=True,\n guidance_train_p=0.1,\n cached_text_enc=True)\n\nA special DiffusionPipeline that accounts for unitary conditions, i.e. compilation.\n\n\n\n\n Back to top" + }, + { + "objectID": "models/unitary_encoder.html", + "href": "models/unitary_encoder.html", + "title": "Encoder for unitaries", + "section": "", + "text": "source\n\n\n\n Unitary_encoder_config (cond_emb_size:int, model_features:list[int],\n num_heads:int, transformer_depths:list[int],\n dropout:float)\n\n\nsource\n\n\n\n\n Unitary_encoder (cond_emb_size, model_features=None, num_heads=8,\n transformer_depths=[4, 4], dropout=0.1)\n\nEncoder for unitary conditions." + }, + { + "objectID": "models/unitary_encoder.html#model-definition", + "href": "models/unitary_encoder.html#model-definition", + "title": "Encoder for unitaries", + "section": "", + "text": "source\n\n\n\n Unitary_encoder_config (cond_emb_size:int, model_features:list[int],\n num_heads:int, transformer_depths:list[int],\n dropout:float)\n\n\nsource\n\n\n\n\n Unitary_encoder (cond_emb_size, model_features=None, num_heads=8,\n transformer_depths=[4, 4], dropout=0.1)\n\nEncoder for unitary conditions." + }, + { + "objectID": "models/transformers.html", + "href": "models/transformers.html", + "title": "Transformers", + "section": "", + "text": "source\n\n\n\n BasisSelfAttnBlock (ch, num_heads, dropout=0)\n\nA self attention block, i.e. a transformer encoder.\n\nsource\n\n\n\n\n BasisCrossAttnBlock (ch, cond_emb_size, num_heads, dropout=0.0)\n\nA cross attention block, i.e. a transformer decoder." + }, + { + "objectID": "models/transformers.html#attention-blocks", + "href": "models/transformers.html#attention-blocks", + "title": "Transformers", + "section": "", + "text": "source\n\n\n\n BasisSelfAttnBlock (ch, num_heads, dropout=0)\n\nA self attention block, i.e. a transformer encoder.\n\nsource\n\n\n\n\n BasisCrossAttnBlock (ch, cond_emb_size, num_heads, dropout=0.0)\n\nA cross attention block, i.e. a transformer decoder." + }, + { + "objectID": "models/transformers.html#spatial-residual-transformers", + "href": "models/transformers.html#spatial-residual-transformers", + "title": "Transformers", + "section": "Spatial residual transformers", + "text": "Spatial residual transformers\n\nsource\n\nSpatialTransformerSelfAttn\n\n SpatialTransformerSelfAttn (ch, num_heads, depth, dropout=0.0)\n\nA spatial residual transformer, only uses self-attention.\n\nsource\n\n\nSpatialTransformer\n\n SpatialTransformer (ch, cond_emb_size, num_heads, depth, dropout=0.0)\n\nA spatial residual transformer, uses self- and cross-attention on conditional input." + }, + { + "objectID": "models/layers.html", + "href": "models/layers.html", + "title": "Layers", + "section": "", + "text": "Common model layers." + }, + { + "objectID": "models/layers.html#basic-scaling-blocks", + "href": "models/layers.html#basic-scaling-blocks", + "title": "Layers", + "section": "Basic scaling blocks", + "text": "Basic scaling blocks\n\nsource\n\nDownBlock2D\n\n DownBlock2D (in_ch, out_ch, kernel_size=2, stride=2, padding=0,\n use_conv=True)\n\nA 2d down scale block.\n\nsource\n\n\nUpBlock2D\n\n UpBlock2D (in_ch, out_ch, kernel_size=2, stride=2, padding=0,\n use_conv=True)\n\nA 2d up scale block." + }, + { + "objectID": "models/layers.html#resnet-blocks", + "href": "models/layers.html#resnet-blocks", + "title": "Layers", + "section": "ResNet blocks", + "text": "ResNet blocks\n\nsource\n\nResBlock2D\n\n ResBlock2D (in_ch, out_ch, kernel_size, skip=True)\n\nA 2d residual block.\n\nsource\n\n\nResBlock2D_Conditional\n\n ResBlock2D_Conditional (in_ch, out_ch, t_emb_size, kernel_size,\n skip=True)\n\nA 2d residual block with input of a time-step \\(t\\) embedding." + }, + { + "objectID": "models/layers.html#feedforward-layer", + "href": "models/layers.html#feedforward-layer", + "title": "Layers", + "section": "FeedForward layer", + "text": "FeedForward layer\n\nsource\n\nFeedForward\n\n FeedForward (in_ch, out_ch, inner_mult=1)\n\nA small dense feed-forward network as used in transformers." + }, + { + "objectID": 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This speeds up training significantly!\n\nsource\n\nCached_OpenClip_Dataset\n\n Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),\n **parameters)\n\nAdds .caching to the Quantum circuit dataset class.\n\n\n\n\n Back to top" + }, + { + "objectID": "inference/infer_compilation.html", + "href": "inference/infer_compilation.html", + "title": "Inference compilation functions", + "section": "", + "text": "source\n\n\n\n split_U_to_tensor (U:numpy.ndarray)\n\n\nsource\n\n\n\n\n get_new_unitary_indices (Us, dataset, silent=False)\n\n\nsource\n\n\n\n\n get_new_unitary_indices_batch (Us, dataset, auto_batch_size=32,\n silent=False, n_jobs=1)" + }, + { + "objectID": "inference/infer_compilation.html#misc", + "href": "inference/infer_compilation.html#misc", + "title": "Inference compilation functions", + "section": "", + "text": "source\n\n\n\n split_U_to_tensor (U:numpy.ndarray)\n\n\nsource\n\n\n\n\n get_new_unitary_indices (Us, dataset, silent=False)\n\n\nsource\n\n\n\n\n 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num_of_qubits, max_gates, g, no_bar=True,\n unique=False, auto_batch_size=512)" + }, + { + "objectID": "inference/infer_srv.html#convertion", + "href": "inference/infer_srv.html#convertion", + "title": "Inference SRV functions", + "section": "Convertion", + "text": "Convertion\n\nsource\n\nconvert_tensors_to_srvs\n\n convert_tensors_to_srvs (out_tensor, gate_pool, sort_srv=False,\n place_barrier=False, n_jobs=1)" + }, + { + "objectID": "inference/infer_srv.html#accuracy", + "href": "inference/infer_srv.html#accuracy", + "title": "Inference SRV functions", + "section": "Accuracy", + "text": "Accuracy\n\nsource\n\nget_srv_accuracy\n\n get_srv_accuracy (svr_list, target_srv)" + }, + { + "objectID": "inference/infer_srv.html#tests", + "href": "inference/infer_srv.html#tests", + "title": "Inference SRV functions", + "section": "Tests", + "text": "Tests\n\nsource\n\ntrue_sample_bin_dist\n\n true_sample_bin_dist (samples_per_bin, bin_size)\n\n\nsource\n\n\ntest_srv_clr_distribution_bin_samples\n\n test_srv_clr_distribution_bin_samples (pipeline, samples_per_bin,\n system_size, num_of_qubits,\n max_gates, g, gate_pool,\n silent=False, device='cpu',\n U=None, prompt_mod:<built-\n infunctioncallable>=<function\n <lambda>>, only_diag=False,\n n_jobs=1)\n\n\nsource\n\n\ntest_srv_clr_distribution\n\n test_srv_clr_distribution (pipeline, samples_per_srv, system_size,\n num_of_qubits, max_gates, g, gate_pool,\n silent=False, device='cpu', U=None,\n prompt_mod:<built-\n infunctioncallable>=<function <lambda>>,\n dist_srvs=None, cond_srvs=None,\n only_diag=False, n_jobs=1)\n\n\nsource\n\n\ntest_guidance_dep\n\n test_guidance_dep (pipeline, srvs, samples, system_size, num_of_qubits,\n max_gates, gs, gate_pool, prompt_mod:<built-\n infunctioncallable>=<function <lambda>>, U=None,\n n_jobs=1)\n\n\nsource\n\n\ntest_srv_acc_vs_length\n\n test_srv_acc_vs_length (pipeline, samples, system_size, num_of_qubits,\n max_gates, g, 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"\f8b8"; } +.bi-database-fill-gear::before { content: "\f8b9"; } +.bi-database-fill-lock::before { content: "\f8ba"; } +.bi-database-fill-slash::before { content: "\f8bb"; } +.bi-database-fill-up::before { content: "\f8bc"; } +.bi-database-fill-x::before { content: "\f8bd"; } +.bi-database-fill::before { content: "\f8be"; } +.bi-database-gear::before { content: "\f8bf"; } +.bi-database-lock::before { content: "\f8c0"; } +.bi-database-slash::before { content: "\f8c1"; } +.bi-database-up::before { content: "\f8c2"; } +.bi-database-x::before { content: "\f8c3"; } +.bi-database::before { content: "\f8c4"; } +.bi-houses-fill::before { content: "\f8c5"; } +.bi-houses::before { content: "\f8c6"; } +.bi-nvidia::before { content: "\f8c7"; } +.bi-person-vcard-fill::before { content: "\f8c8"; } +.bi-person-vcard::before { content: "\f8c9"; } +.bi-sina-weibo::before { content: "\f8ca"; } +.bi-tencent-qq::before { content: "\f8cb"; } +.bi-wikipedia::before { content: "\f8cc"; } diff --git 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document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

    ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const 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t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center 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b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return 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s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..619c57c --- /dev/null +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof 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0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..f5d852d --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..698ab1e --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,139 @@ + + + + https://FlorianFuerrutter.github.io/genQC/printing.html + 2023-11-10T20:16:28.232Z + + + https://FlorianFuerrutter.github.io/genQC/inference/infer_misc.html + 2023-11-10T20:16:27.480Z + + + https://FlorianFuerrutter.github.io/genQC/inference/infer_gate_hist.html + 2023-11-10T20:16:26.744Z + + + https://FlorianFuerrutter.github.io/genQC/dataset/qc_dataset.html + 2023-11-10T20:16:26.060Z + + + https://FlorianFuerrutter.github.io/genQC/dataset/mixed_cached_qc_dataset.html + 2023-11-10T20:16:25.396Z + + + https://FlorianFuerrutter.github.io/genQC/scheduler/scheduler.html + 2023-11-10T20:16:24.716Z + + + https://FlorianFuerrutter.github.io/genQC/scheduler/scheduler_ddim.html + 2023-11-10T20:16:24.068Z + + + https://FlorianFuerrutter.github.io/genQC/util.html + 2023-11-10T20:16:23.364Z + + + https://FlorianFuerrutter.github.io/genQC/models/frozen_open_clip.html + 2023-11-10T20:16:22.620Z + + + https://FlorianFuerrutter.github.io/genQC/models/unet_qc.html + 2023-11-10T20:16:21.908Z + + + https://FlorianFuerrutter.github.io/genQC/models/config_model.html + 2023-11-10T20:16:21.212Z + + + https://FlorianFuerrutter.github.io/genQC/pipeline/pipeline.html + 2023-11-10T20:16:20.548Z + + + https://FlorianFuerrutter.github.io/genQC/index.html + 2023-11-10T20:16:19.868Z + + + https://FlorianFuerrutter.github.io/genQC/examples/hello_circuit.html + 2023-11-10T20:16:18.532Z + + + https://FlorianFuerrutter.github.io/genQC/metrics.html + 2023-11-10T20:16:17.556Z + + + https://FlorianFuerrutter.github.io/genQC/platform/qcircuit_metrics.html + 2023-11-10T20:16:16.792Z + + + https://FlorianFuerrutter.github.io/genQC/platform/simulation/qcircuit_sim.html + 2023-11-10T20:16:16.084Z + + + https://FlorianFuerrutter.github.io/genQC/platform/qcircuit_evaluation.html + 2023-11-10T20:16:15.308Z + + + https://FlorianFuerrutter.github.io/genQC/platform/qcircuit_util.html + 2023-11-10T20:16:16.440Z + + + https://FlorianFuerrutter.github.io/genQC/platform/qcircuit_dataset_construction.html + 2023-11-10T20:16:17.188Z + + + https://FlorianFuerrutter.github.io/genQC/examples/editing_and_masking.html + 2023-11-10T20:16:18.080Z + + + https://FlorianFuerrutter.github.io/genQC/examples/unitary_compilation.html + 2023-11-10T20:16:19.056Z + + + https://FlorianFuerrutter.github.io/genQC/pipeline/diffusion_pipeline.html + 2023-11-10T20:16:20.216Z + + + https://FlorianFuerrutter.github.io/genQC/pipeline/diffusion_pipeline_special.html + 2023-11-10T20:16:20.884Z + + + https://FlorianFuerrutter.github.io/genQC/models/unitary_encoder.html + 2023-11-10T20:16:21.536Z + + + https://FlorianFuerrutter.github.io/genQC/models/transformers.html + 2023-11-10T20:16:22.252Z + + + https://FlorianFuerrutter.github.io/genQC/models/layers.html + 2023-11-10T20:16:22.984Z + + + https://FlorianFuerrutter.github.io/genQC/config_loader.html + 2023-11-10T20:16:23.744Z + + + https://FlorianFuerrutter.github.io/genQC/scheduler/scheduler_ddpm.html + 2023-11-10T20:16:24.400Z + + + https://FlorianFuerrutter.github.io/genQC/dataset/dataset_helper.html + 2023-11-10T20:16:25.076Z + + + https://FlorianFuerrutter.github.io/genQC/dataset/config_dataset.html + 2023-11-10T20:16:25.720Z + + + https://FlorianFuerrutter.github.io/genQC/dataset/cached_qc_dataset.html + 2023-11-10T20:16:26.416Z + + + https://FlorianFuerrutter.github.io/genQC/inference/infer_compilation.html + 2023-11-10T20:16:27.148Z + + + https://FlorianFuerrutter.github.io/genQC/inference/infer_srv.html + 2023-11-10T20:16:27.852Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/util.html b/util.html new file mode 100644 index 0000000..8f95302 --- /dev/null +++ b/util.html @@ -0,0 +1,926 @@ + + + + + + + + + +genQC - Util + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +     + +
    + + +
    + + + +
    + +
    +
    +

    Util

    +
    + + + +
    + + + + +
    + + +
    + + +

    Miscellaneous util code.

    +
    +

    Memory utils

    +
    +

    source

    +
    +

    MemoryCleaner

    +
    +
     MemoryCleaner ()
    +
    +

    CLass with static methods to clean (gpu) memory.

    +
    +
    +
    +

    Python utils

    +
    +

    source

    +
    +

    virtual

    +
    +
     virtual (f:<built-infunctioncallable>)
    +
    +

    Decorator to enfore subclass method implementations and raises error at method calls.

    +
    +
    class A():
+    def p1(self, x): print("A p1", x)
+    
+    @virtual
+    def p2(self, x): pass
+ 
+class B(A):
+    def p3(self, x): print("B p2", x)
+    
+b = B()
+b.p1(1)
+try:
+    b.p2(1)
+except BaseException as e:
+    print("Exception that would be raised: ", e)
    +
    +
    A p1 1
+Exception that would be raised:  Virtual method p2 needs to be implemented by subclass B.
    +
    +
    +
    +
    +
    +

    Torch utils

    +
    +

    source

    +
    +

    DataLoaders

    +
    +
     DataLoaders (*dls:list[torch.utils.data.dataloader.DataLoader])
    +
    +

    Combines train and valid DataLoader.

    +
    +

    source

    +
    +
    +

    infer_torch_device

    +
    +
     infer_torch_device ()
    +
    +
    +
    infer_torch_device()
    +
    +
    [INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.
    +
    +
    +
    device(type='cuda')
    +
    +
    +
    +

    source

    +
    +
    +

    number_of_paramters

    +
    +
     number_of_paramters (model:torch.nn.modules.module.Module)
    +
    +
    +

    source

    +
    +
    +

    scale_tensor

    +
    +
     scale_tensor (t:torch.Tensor)
    +
    +

    [-1,1] to [0,1]

    +
    +

    source

    +
    +
    +

    normalize_tensor

    +
    +
     normalize_tensor (t:torch.Tensor)
    +
    +

    [0,1] to [-1,1]

    +
    +
    +
    +

    Plot utils

    +
    +

    source

    +
    +

    saveSvg

    +
    +
     saveSvg (filename)
    +
    +
    +

    source

    +
    +
    +

    savePng

    +
    +
     savePng (filename)
    +
    +
    +

    source

    +
    +
    +

    savePdf

    +
    +
     savePdf (filename)
    +
    +
    +

    source

    +
    +
    +

    plot_image_grid

    +
    +
     plot_image_grid (imgs:Union[list,<built-infunctionarray>,torch.Tensor],
+                  labels:list=None, labels_fs='medium', figsize=(16, 4),
+                  cols=8, cmap='Greys', show_colorbar=False,
+                  **imshow_kwargs)
    +
    +
    +
    n = 6
+plot_image_grid(torch.randn((n,28,28,1)), [f"label {i}" for i in range(n)])
    +
    +

    +
    +
    +
    +

    source

    +
    +
    +

    latents_to_pil

    +
    +
     latents_to_pil (latents:torch.Tensor, channels=None)
    +
    + + +
    +
    + + Back to top
    + +
    + + + + \ No newline at end of file diff --git a/util_files/figure-html/cell-15-output-1.png b/util_files/figure-html/cell-15-output-1.png new file mode 100644 index 0000000000000000000000000000000000000000..89279760bdee42f418ae97e00f46fcde6dc616f9 GIT binary patch literal 26947 zcmc(Ic~sNqwzjQpt+m$ETJ=~Q>V)C|)krKFz&Z~EYjHvuT1|qQAZkPiV{NO}Dk`%f z8fw*8D+UcJgwZ-6Q%DshMj2EzQ9%YHAToW=e$(D{&bQY6<6HNheqHOVb1Vo+e($^Y zv!7v~Jzu)d@B7{-@Ac@>qwm56bH3`)rVfDDH*S$BQWz%=>ngNmncJUl9J4s#{K13&uwT)mBXM5TCqiA4W7@C3j@$dwPnz;GYwk7V zb$9t4c>Deb9RIshPp?T=Kh`M>TOSt*9mVD6n`*9i8`r&&F`Iw*^Bc2f%}SA)OCwvx z^{D@B-Szz!4jw!h*|wzguv2&4xklG->D73qiK3e8jAgHzYyA|{0xp&wnX$%Pdc^wDDyV`Zw(g_|c9-#h!wm6Xj7E=RU}-lP7@;)F?QO6SnK_=5lQyBAiZtePyo zck#?n*N6`Nu)dduj`qzBYk77$_3qsZV*h&={aXq~9k?^<0B^0utnW^n-*fYBr?6LN zb6!2%Tb|;X<{J8OkDy-*3KlG0wCJ@t@}vI!yX&Iq^~HSpSwu%Ly~nw8=B#>fdFS)r zdksJDanil>!zo@=;{rWxp2``}6{R3GZ<-L0ZU7QhkCL^Tzw@Uod7mtfu z1M=SawnREsJz&SoO9LHzB%v*J=S(_9cXU9%nYJ0R<$FfV-nHuY@7_M+n-?K(I`c-s zoS>Vlx3s+wPFVWO4+AEwD5=NQzo(V@iNf|~l)cp$=`N8-zP^8HM|$ULoVK2#ff?JL ze)Z`;-hC^<-usi!(k@-y)4$tz{P^*=E?>U=nF*7fs^UVWz0(se`c$Pgq zHGKN1hRj&QPdJplaDfKA+qZAuM+MQNJ`

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