Merge pull request #302 from sQUlearn/develop

Release 0.8.0

Author: MoritzWillmann

.github/workflows/doc_checks.yml

@@ -16,5 +16,5 @@ jobs:
         - uses: sQUlearn/sphinx-action@master
           with:
             docs-folder: "docs/"
-            pre-build-command: "pip3 install ."
+            pre-build-command: "pip3 install .[docs]"
             build-command: "sphinx-build -b html . _build -W --keep-going"

.github/workflows/publish_docs.yml

@@ -24,7 +24,7 @@ jobs:
             uses: sQUlearn/sphinx-action@master
             with:
               docs-folder: "docs/"
-              pre-build-command: "pip3 install ."
+              pre-build-command: "pip3 install .[docs]"
               build-command: "sphinx-build -b html . _build -W"
           - name: Commit Files
             run: |

.github/workflows/tests.yaml

@@ -5,24 +5,120 @@ on:
     - cron: '23 1 * * *'
 
 jobs:
-  pytest:
+  pytest_minimal_requirements:
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: '3.9'
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
+          python -m venv venv_minimal
+          source venv_minimal/bin/activate
+          pip install toml
+          python3 install_lowest_dependencies.py
+      - name: Test with pytest
+        run: |
+          source venv_minimal/bin/activate
+          pip install pytest
+          pytest tests/
+      - name: Test examples
+        run: |
+          source venv_minimal/bin/activate
+          pip install nbmake
+          pytest --nbmake --nbmake-timeout 600 examples/
+      - name: clean
+        run: |
+          rm -rf venv_minimal
+
+  pytest_qiskit_0_46:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m venv venv_qiskit_0_46
+          source venv_qiskit_0_46/bin/activate
+          pip install .[examples] qiskit==0.46.3 qiskit-ibm-runtime==0.20.0
+      - name: Test with pytest
+        run: |
+          source venv_qiskit_0_46/bin/activate
+          pip install pytest
+          pytest tests/
+      - name: Test examples
+        run: |
+          source venv_qiskit_0_46/bin/activate
+          pip install nbmake
+          pytest --nbmake --nbmake-timeout 600 examples/
+      - name: clean
+        run: |
+          rm -rf venv_qiskit_0_46
+
+  pytest_qiskit_1:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m venv venv_qiskit_1
+          source venv_qiskit_1/bin/activate
+          pip install .[examples] qiskit==1.1.2 qiskit-ibm-runtime==0.27.1
+      - name: Test with pytest
+        run: |
+          source venv_qiskit_1/bin/activate
+          pip install pytest
+          pytest tests/
+      - name: Test examples
+        run: |
+          source venv_qiskit_1/bin/activate
+          pip install nbmake
+          pytest --nbmake --nbmake-timeout 600 examples/
+      - name: clean
+        run: |
+          rm -rf venv_qiskit_1
+
+  pytest_latest: 
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: |
+            3.12
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m venv venv_latest
+          source venv_latest/bin/activate
           pip install .[examples]
       - name: Test with pytest
         run: |
+          source venv_latest/bin/activate
           pip install pytest
           pytest tests/
       - name: Test examples
         run: |
+          source venv_latest/bin/activate
           pip install nbmake
-          pytest --nbmake --nbmake-timeout 600 examples/ 
+          pytest --nbmake --nbmake-timeout 600 examples/
+      - name: clean
+        run: |
+          rm -rf venv_latest

.gitignore

@@ -169,7 +169,7 @@ examples/qnn/*_log.csv
 examples/**/*.log
 
 #Example caches
-examples/**/_cache
+**/_cache
 examples/integration/mlruns
 
 # Genereated doc images

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

@@ -9,14 +9,20 @@ Prerequisites
 sQUlearn requires a recent python3 (>=3.9) installation.
 Additionally the following python packages are necessary: ::
 
-    numpy>=1.17
-    scipy>=1.5
-    scikit-learn>=1.0
-    qiskit>=0.42.1
+    bayesian-optimization>=1.4.3,<2
+    dill>=0.3.4
+    mapomatic>=0.10.0
+    networkx>=3.0
+    numpy>=1.20
+    pennylane>=0.34.0
+    qiskit>=0.45.0
     qiskit-aer>=0.12.0
-    qiskit-machine-learning>=0.6.0
-    qiskit-ibm-runtime>=0.9
-    dill>=0.3
+    qiskit-algorithms>=0.3.0
+    qiskit-ibm-runtime>=0.18.0
+    qiskit-machine-learning>=0.7.0
+    scipy>=1.8.0
+    scikit-learn>=1.2.0,<1.4.2
+    tqdm>=4.1.0
 
 The packages are automatically installed when installing sQUlearn with pip.
 
@@ -48,3 +54,28 @@ To install the latest development version:
 .. code-block:: bash
 
     pip install git+https://github.com/sQUlearn/squlearn.git@develop
+
+
+
+Installation with optional dependencies:
+----------------------------------------
+
+There are several optional dependencies that can be installed with sQUlearn.
+To install sQUlearn with the dependencies usefull for development, run the following command:
+
+.. code-block:: bash
+
+    pip install squlearn[dev]
+
+To install sQUlearn with the dependencies necessary to run all examples, run the following command:
+
+.. code-block:: bash
+
+    pip install squlearn[examples]
+
+And to install sQUlearn with the dependencies necessary to build the documentation,
+run the following command:
+
+.. code-block:: bash
+
+    pip install squlearn[docs]

docs/install/install.rst

@@ -20,6 +20,7 @@ QML Regressors
    kernel.ml.QKRR
    kernel.ml.QGPR
    qnn.QNNRegressor
+   qrc.QRCRegressor
 
 
 QML Classifiers
@@ -35,7 +36,7 @@ QML Classifiers
    kernel.ml.QSVC
    kernel.ml.QGPC
    qnn.QNNClassifier
-
+   qrc.QRCClassifier
 
 Circuit Design
 ====================================
@@ -64,8 +65,12 @@ Encoding Circuits
    encoding_circuit.MultiControlEncodingCircuit
    encoding_circuit.ChebyshevRx
    encoding_circuit.ParamZFeatureMap
+   encoding_circuit.KyriienkoEncodingCircuit
    encoding_circuit.QiskitEncodingCircuit
    encoding_circuit.QCNNEncodingCircuit
+   encoding_circuit.RandomLayeredEncodingCircuit
+   encoding_circuit.RandomEncodingCircuit
+
 
 Encoding Circuit Tools
 ------------------------------------
@@ -140,6 +145,8 @@ Execution Tools
    :template: class.rst
 
    Executor
+   util.executor.ExecutorEstimator
+   util.executor.ExecutorSampler
 
 
 Core
@@ -162,7 +169,7 @@ Quantum Kernel Core
    kernel.matrix.FidelityKernel
    kernel.matrix.ProjectedQuantumKernel
 
-.. automodule:: squlearn.kernel.optimization
+.. automodule:: squlearn.kernel.loss
     :no-members:
     :no-inherited-members:
 
@@ -173,9 +180,8 @@ Quantum Kernel Core
    :toctree: generated/
    :template: class.rst
 
-   kernel.optimization.kernel_optimizer.KernelOptimizer
-   kernel.optimization.negative_log_likelihood.NLL
-   kernel.optimization.target_alignment.TargetAlignment
+   kernel.loss.negative_log_likelihood.NLL
+   kernel.loss.target_alignment.TargetAlignment
 
 QNN Core
 ------------------------------------
@@ -198,6 +204,7 @@ QNN Core
    qnn.lowlevel_qnn_base.LowLevelQNNBase
    qnn.loss.SquaredLoss
    qnn.loss.VarianceLoss
+   qnn.loss.ODELoss
    qnn.loss.ParameterRegularizationLoss
 
 Tools for training QNNs
@@ -280,11 +287,11 @@ Base Classes
     observables.observable_base.ObservableBase
     encoding_circuit.encoding_circuit_base.EncodingCircuitBase
     kernel.matrix.kernel_matrix_base.KernelMatrixBase
-    kernel.optimization.kernel_loss_base.KernelLossBase
-    kernel.optimization.kernel_optimization_base.KernelOptimizerBase
+    kernel.loss.kernel_loss_base.KernelLossBase
     optimizers.optimizer_base.OptimizerBase
     qnn.base_qnn.BaseQNN
     qnn.loss.LossBase
+    qrc.base_qrc.BaseQRC