Integration of HQS automatic backend selection (#234)

* added files from hqs

* black format

* wip

* first version of automatic selection is working

* modified parallel primitves

* wip

* wip

* wip

* wip

* wip

* wip

* cleaning

* wip

* Add more comments

* bug fix

* removed unimportant code

* fixed test

* wip

* ip

* doc strings fixed

* added test for parallel execution

* get everything working with fake backends

* added more tests for automatic selection

* add missng testjob

* update off docu and dependencies

* deleted tests

* reset of regression_example

* black

* fixed testjobs

* adjust testjobs

* black

* remove value checks in tests, since they are not reproduceable

* black

* remove result check from test_parallel_primtives.py

* cleaning of test

* black

* update executor graphic

* update of the doc

* fix of primitive

* removed print statement

* final run of black

* fix test

* fix some stuff

* Update src/squlearn/util/executor.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* Update src/squlearn/util/execution/backend_auto_selection.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* Update src/squlearn/util/execution/backend_auto_selection.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* Update src/squlearn/qnn/qnnr.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* Update src/squlearn/qnn/qnnc.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* Update src/squlearn/qnn/lowlevel_qnn_qiskit.py

Co-authored-by: Moritz <44642314+MoritzWillmann@users.noreply.github.com>

* fix in test

* renamed AutoSelBack

* fix pyproject.toml

* fix pyproject.toml v2

* fix pyproject.toml v3

* update of testjob

* black

* fix for testjobs

* fix test

* rechange dependencies

* remove IBMQuantum variable in the executor

* fix executor docstring

* black

* add docstring for select_backend, remove comments

* rename function

* change ValueError to TypeError

* fix if clauses

* update parallel primitives

* rename `parse_openqasm`

* clean backend auto selection code

* black

* fix in Qiskit lowleven QNN and fix of IBMQuantum variable in the executor

* black

* remove metric

* remove result comparison in parallel primitives

* make `heuristic` function and add tests

* black

* black tests

* rename remote and ibmquantum

* clean backend auto selection

* add hqaa modes

---------

Co-authored-by: Moritz <moritz.willmann@ipa.fraunhofer.de>

Author: David-Kreplin

README.md

@@ -2,7 +2,7 @@
   <img alt="sQUlearn" src="https://raw.githubusercontent.com/sQUlearn/squlearn/main/docs/_static/logo.png" />
 </p>
 
-sQUlearn is a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.
+sQUlearn is a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive tool set that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.
 
 sQUlearn offers scikit-learn compatible high-level interfaces for various kernel methods and QNNs. They build on top of the low-level interfaces of the QNN engine and the quantum kernel engine. The executor is used to run experiments on simulated and real backends of the Qiskit or PennyLane frameworks.
 
@@ -39,7 +39,7 @@ pip install .[examples]
 ```
 
 ## Contribute to sQUlearn
-Thanks for considering to contribute to sQUlearn! Please read our [contribution guidelines](https://github.com/sQUlearn/squlearn/blob/main/.github/CONTRIBUTING.md) before you submit a pull request.
+Thanks for considering contributing to sQUlearn! Please read our [contribution guidelines](https://github.com/sQUlearn/squlearn/blob/main/.github/CONTRIBUTING.md) before you submit a pull request.
 
 ---
 ## License
@@ -61,11 +61,6 @@ For general questions regarding sQUlearn, use the [GitHub Discussions](https://g
 ---
 ## Acknowledgements
 
-This project was supported by the German Federal Ministry
-of Economic Affairs and Climate Action through the
-projects AutoQML (grant no. 01MQ22002A) and AQUAS
-(grant no. 01MQ22003D), as well as the German Federal 
-Ministry of Education and Research through the project
-H2Giga Degrad-EL3 (grant no. 03HY110D).
+This project was supported by the German Federal Ministry of Economic Affairs and Climate Action through the projects AutoQML (grant no. 01MQ22002A) and AQUAS (grant no. 01MQ22003D), as well as the German Federal Ministry of Education and Research through the project H2Giga Degrad-EL3 (grant no. 03HY110D).
 
----
+---

docs/_static/util/executor.png

@@ -106,10 +106,12 @@ def setup(app):
 
 # intersphinx
 intersphinx_mapping = {
-    "qiskit": ("https://docs.quantum.ibm.com/api/qiskit/", None),
+    "pennylane": ("https://docs.pennylane.ai/en/stable/", None),
+    "qiskit": ("https://docs.quantum.ibm.com/api/qiskit/1.1/", None),
     "qiskit-aer": ("https://qiskit.github.io/qiskit-aer/", None),
+    "qiskit-ibm-runtime": ("https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/0.27/", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/", None),
-    "sklearn": ("https://scikit-learn.org/stable/", None),
+    "sklearn": ("https://scikit-learn.org/1.4/", None),
 }
 
 suppress_warnings = ["myst.header", "config.cache"]

docs/conf.py

@@ -145,6 +145,8 @@ Execution Tools
    :template: class.rst
 
    Executor
+   util.executor.ExecutorEstimator
+   util.executor.ExecutorSampler
 
 
 Core

docs/modules/classes.rst

@@ -25,17 +25,19 @@ classifiers = [
 ]
 keywords = ["quantum", "machine learning", "qml"]
 dependencies = [
+    "bayesian-optimization>=1.4.3",
     "dill>=0.3",
-    "numpy>=1.17,<2.0",
-    "qiskit>=0.43.0,<1.1.0",
+    "mapomatic>=0.10.0",
+    "networkx>=3.0",
+    "numpy>=1.17",
+    "pennylane>=0.34.0",
+    "qiskit>=0.43.0,<1.2.0",
     "qiskit-aer>=0.12.0",
     "qiskit-algorithms>=0.3.0",
-    "qiskit-ibm-runtime>=0.15.1",
+    "qiskit-ibm-runtime>=0.15.1,<0.28.0",
     "qiskit-machine-learning>=0.6.1",
-    "pennylane>=0.34.0",
     "scipy>=1.5",
     "scikit-learn>=1.0,<1.4.2",
-    "bayesian-optimization>=1.4.3",
     "tqdm>=4.0",
 ]
 requires-python = ">=3.9"
@@ -60,6 +62,7 @@ examples = [
 ]
 docs = [
     "ipykernel",
+    "jupyter-sphinx",
     "matplotlib>=3.5",
     "myst-parser",
     "nbsphinx",
@@ -69,7 +72,6 @@ docs = [
     "sphinxcontrib-spelling",
     "sphinx-rtd-theme<3.0.0",
     "sphinx-sitemap",
-    "jupyter-sphinx",
 ]
 
 [project.urls]

docs/user_guide/executor.rst

@@ -116,6 +116,11 @@ def __init__(
             self._enc_circ = self._encoding_circuit.get_circuit(
                 self._feature_vector, self._parameter_vector
             )
+
+            # Automatic select backend if not chosen
+            if not self._executor.backend_chosen:
+                self._enc_circ, _ = self._executor.select_backend(self._enc_circ)
+
             if self._executor.is_statevector:
                 if self._parameter_vector is None:
                     self._quantum_kernel = FidelityStatevectorKernel(

pyproject.toml

@@ -45,6 +45,7 @@ def set_map(self, qubit_map: Union[list, dict], num_all_qubits: int):
                                            qubits are mapped to the output qubits.
             num_all_qubits (int): The total number of qubits in the system.
         """
+
         self._qubit_map = qubit_map
         self._num_all_qubits = num_all_qubits
         self._is_mapped = True

src/squlearn/kernel/matrix/fidelity_kernel.py

@@ -44,6 +44,11 @@ class BaseQNN(BaseEstimator, ABC):
         callback (Union[Callable, str, None], default=None): A callback for the optimization loop.
             Can be either a Callable, "pbar" (which uses a :class:`tqdm.tqdm` process bar) or None.
             If None, the optimizers (default) callback will be used.
+        primitive : The Qiskit primitive that is utilized in the qnn, if a Qiskit backend
+                    is used in the executor (not supported for PennyLane backends)
+                    Default primitive is the one specified in the executor initialization,
+                    if nothing is specified, the estimator will used.
+                    Possible values are ``"estimator"`` or ``"sampler"``.
     """
 
     def __init__(
@@ -65,6 +70,7 @@ def __init__(
         caching: bool = True,
         pretrained: bool = False,
         callback: Union[Callable, str, None] = None,
+        primitive: Union[str, None] = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -117,6 +123,7 @@ def __init__(
         self.caching = caching
         self.pretrained = pretrained
 
+        self.primitive = primitive
         self.executor = executor
 
         self.shot_control = shot_control
@@ -324,7 +331,11 @@ def _fit(self, X, y, weights: np.ndarray = None) -> None:
 
     def _initialize_lowlevel_qnn(self):
         self._qnn = LowLevelQNN(
-            self.encoding_circuit, self.operator, self.executor, caching=self.caching
+            self.encoding_circuit,
+            self.operator,
+            self.executor,
+            caching=self.caching,
+            primitive=self.primitive,
         )
 
     def _validate_input(self, X, y, incremental, reset):

src/squlearn/observables/observable_base.py

@@ -34,6 +34,9 @@ def __new__(
     ):
 
         if executor.quantum_framework == "pennylane":
+            if "primitive" in kwargs:
+                RuntimeError("Warning: Primitive argument is not supported for PennyLane")
+                kwargs.pop("primitive")
             return LowLevelQNNPennyLane(
                 parameterized_quantum_circuit, observable, executor, *args, **kwargs
             )