Noise module (#25)

* Init

* Fixed Problems in PresetQiskitNoisyBackend.py

* Clean up in PresetQiskitNoisyBackend.py and CustomQiskitNoisyBackend.py

* Deactivated CircuitCopulaIBM

* Revised IBM Quantum Deployment. I would suggest to revise it in Q3/2024, since IBM will change a lot in the near future.

* MMD Update

* MMD Update - QCBM removed for now

* renamed IBM Backends

* removed plt.ioff statement

* Added ibm_runtime to get_requirements and removed redundant input

* Update requirements_full.txt

* typo

* Adjusted qiskit package versions.

* Avoid lynting errors.

* Avoid lynting error caused by dependencies.

* Hopefully solved the dependency issues for now.

* Addressing dependency issues by temporarily removing ibm-runtime-dependent content.

* Updated moduledb and removed remaining ibm_runtime-dependent parts.

---------

Co-authored-by: Maximilian Wolf <[email protected]>
Co-authored-by: Marvin Erdmann (FG-231) <[email protected]>

.settings/requirements_full.txt

@@ -1,30 +1,32 @@
-seaborn==0.13.0
-networkx==2.8.8
-inquirer==3.1.2
-packaging==23.1
-pyyaml==6.0
-typing-extensions==4.6.3
-sphinx==6.2.1
-sphinx-rtd-theme==1.2.0
-numpy==1.23.5
-dimod==0.12.5
-amazon-braket-sdk==1.35.1
-scipy==1.11.1
-botocore==1.25.7
-boto3==1.22.7
-pennylane==0.28.0
-pennylane-lightning==0.28.0
-amazon-braket-pennylane-plugin==1.5.2
-dwave-samplers==1.0.0
-nnf==0.4.1
-qubovert==1.2.5
-python-sat==0.1.7.dev26
-more-itertools==9.0.0
-qiskit-optimization==0.5.0
-pyqubo==1.4.0
-dwave_networkx==0.8.13
-qiskit==0.40.0
-pandas==1.5.2
-cma==3.3.0
-tensorboard==2.13.0
-tensorboardX==2.6.2
+seaborn==0.13.0
+networkx==2.8.8
+inquirer==3.1.2
+packaging==23.1
+pyyaml==6.0
+typing-extensions==4.6.3
+sphinx==6.2.1
+sphinx-rtd-theme==1.2.0
+numpy==1.23.5
+dimod==0.12.5
+amazon-braket-sdk==1.35.1
+scipy==1.11.1
+botocore==1.25.7
+boto3==1.22.7
+pennylane==0.28.0
+pennylane-lightning==0.28.0
+amazon-braket-pennylane-plugin==1.5.2
+dwave-samplers==1.0.0
+nnf==0.4.1
+qubovert==1.2.5
+python-sat==0.1.7.dev26
+more-itertools==9.0.0
+qiskit-optimization==0.5.0
+pyqubo==1.4.0
+dwave_networkx==0.8.13
+qiskit==0.45.0
+pandas==1.5.2
+qiskit-ibmq-provider==0.19.2
+cma==3.3.0
+tensorboard==2.13.0
+tensorboardX==2.6.2
+qiskit_aer==0.11.2

src/modules/applications/QML/generative_modeling/data/data_handler/DataHandler.py

@@ -14,6 +14,7 @@
 
 import pickle
 import os
+from qiskit import qpy
 
 import numpy as np
 import pandas as pd
@@ -25,7 +26,7 @@
 
 class DataHandler(Core, ABC):
     """
-    The task of the DataHandler module is to translate the application’s data 
+    The task of the DataHandler module is to translate the application’s data
     and problem specification into preproccesed format.
     """
 
@@ -150,6 +151,10 @@ def postprocess(self, input_data: dict, config: dict, **kwargs):
             with open(f"{store_dir_iter}/training_results-{kwargs['rep_count']}.pkl", 'wb') as f:
                 pickle.dump(input_data, f)
 
+            if "circuit_transpiled" in list(input_data.keys()):
+                with open(f"{store_dir_iter}/transpiled_circuit_{kwargs['rep_count']}.qpy", 'wb') as f:
+                    qpy.dump(input_data.pop("circuit_transpiled"), f)
+
             # Save variables transformed space
             if "Transformation" in list(input_data.keys()):
                 self.metrics.add_metric_batch({"KL_best": input_data["KL_best_transformed"]})
@@ -164,7 +169,7 @@ def postprocess(self, input_data: dict, config: dict, **kwargs):
     @abstractmethod
     def data_load(self, gen_mod: dict, config: dict) -> dict:
         """
-        Helps to ensure that the model can effectively learn the underlying 
+        Helps to ensure that the model can effectively learn the underlying
         patterns and structure of the data, and produce high-quality outputs.
 
         :param gen_mod: dictionary with collected information of the previous modules
@@ -207,12 +212,12 @@ def evaluate(self, solution: any) -> (dict, float):
     @staticmethod
     def tb_to_pd(logdir: str, rep: str) -> None:
         """
-        Converts TensorBoard event files in the specified log directory 
+        Converts TensorBoard event files in the specified log directory
         into a pandas DataFrame and saves it as a pickle file.
 
         :param logdir: path to the log directory containing TensorBoard event files
-        :type logdir: str 
-        
+        :type logdir: str
+
         """
         event_acc = EventAccumulator(logdir)
         event_acc.Reload()