Framework update (#26)

* Update code * Update code * Update code * Update code * Update documentation * Update test * Update .tolm * Try to fix problem in model load * Try to fix problem in model load * 🎨 pre-commit fixes * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * Try to fix problem in model load * 🎨 pre-commit fixes * Try to fix problem in model load * Try to fix problem in model load * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update workflow * Update code * clean Nox * bib * ⬆️ update pre-commit hooks * ✏️ fix typos * 👷 add artifact attestations * 🔧 update dependencies (drop numpy upper cap) * ✨ add 3.13 support * 🚨🎨 code quality fixes * 🔧 loosen sphinx dependency * bib * 🩹 actually cite the reference --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: burgholzer <burgholzer@me.com>
cda-tum · Aug 13, 2024 · 61c7860 · 61c7860
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diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml
@@ -3,11 +3,14 @@ on:
   release:
     types: [published]
   workflow_dispatch:
+  pull_request:
+    paths:
+      - .github/workflows/cd.yml
 
 jobs:
   python-packaging:
     name: 🐍 Packaging
-    uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-packaging.yml@v1.2.1
+    uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-packaging.yml@v1.3
     with:
       pure-python: true
 
@@ -20,11 +23,17 @@ jobs:
       url: https://pypi.org/p/mqt.qao
     permissions:
       id-token: write
+      attestations: write
+      contents: read
     needs: [python-packaging]
     steps:
       - uses: actions/download-artifact@v4
         with:
           pattern: cibw-*
           path: dist
           merge-multiple: true
+      - name: Generate artifact attestation for sdist and wheel(s)
+        uses: actions/attest-build-provenance@v1.4.0
+        with:
+          subject-path: "dist/*"
       - uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -14,19 +14,21 @@ concurrency:
 jobs:
   change-detection:
     name: 🔍 Change
-    uses: cda-tum/mqt-workflows/.github/workflows/reusable-change-detection.yml@v1.2.1
+    uses: cda-tum/mqt-workflows/.github/workflows/reusable-change-detection.yml@v1.3
 
   python-tests:
     name: 🐍 Test
     needs: change-detection
     if: fromJSON(needs.change-detection.outputs.run-python-tests)
-    uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-ci.yml@v1.2.1
+    uses: cda-tum/mqt-workflows/.github/workflows/reusable-python-ci.yml@v1.3
+    with:
+      skip-testing-latest-python: true
 
   code-ql:
     name: 📝 CodeQL
     needs: change-detection
     if: fromJSON(needs.change-detection.outputs.run-code-ql)
-    uses: cda-tum/mqt-workflows/.github/workflows/reusable-code-ql-python.yml@v1.2.1
+    uses: cda-tum/mqt-workflows/.github/workflows/reusable-code-ql-python.yml@v1.3
 
   required-checks-pass: # This job does nothing and is only used for branch protection
     name: 🚦 Check

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -85,18 +85,17 @@ repos:
         additional_dependencies: [black==24.*]
 
   # Format configuration files with prettier
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v4.0.0-alpha.8
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.3.3
     hooks:
       - id: prettier
         types_or: [yaml, markdown, html, css, scss, javascript, json]
 
   # Check for spelling
-  - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.23.6
     hooks:
-      - id: codespell
-        args: ["-L", "wille,linz", "--skip", "*.ipynb"]
+      - id: typos
 
   # Catch common capitalization mistakes
   - repo: local
@@ -113,3 +112,17 @@ repos:
     hooks:
       - id: sp-repo-review
         additional_dependencies: ["repo-review[cli]"]
+
+  # Check JSON schemata
+  - repo: https://github.com/python-jsonschema/check-jsonschema
+    rev: 0.29.1
+    hooks:
+      - id: check-dependabot
+      - id: check-github-workflows
+      - id: check-readthedocs
+
+  # Check the pyproject.toml file
+  - repo: https://github.com/henryiii/validate-pyproject-schema-store
+    rev: 2024.08.08
+    hooks:
+      - id: validate-pyproject
diff --git a/CITATION.bib b/CITATION.bib
@@ -7,3 +7,13 @@ @INPROCEEDINGS{volpe2024towards
 	PRIMARYCLASS  = {quant-ph},
 	ARCHIVEPREXIX = {arxiv},
 }
+
+@INPROCEEDINGS{volpe2024predictive,
+	AUTHOR        = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},
+	TITLE         = {{A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem}},
+	YEAR          = {2024},
+	BOOKTITLE     = {IEEE International Conference on Quantum Computing and Engineering (QCE)},
+	EPRINT        = {2408.03613},
+	PRIMARYCLASS  = {quant-ph},
+	ARCHIVEPREXIX = {arxiv},
+}
diff --git a/README.md b/README.md
@@ -62,7 +62,7 @@ solution = Solver().solve_Dwave_quantum_annealer(prb, token=token)
 
 ## References
 
-In case you are using the MQT Quantum Auto Optimizer in your work, we would be thankful if you referred to it by citing the following publication:
+In case you are using the MQT Quantum Auto Optimizer in your work, we would be thankful if you referred to it by citing the following publications:
 
 ```bibtex
 @INPROCEEDINGS{volpe2024towards,
@@ -74,6 +74,16 @@ In case you are using the MQT Quantum Auto Optimizer in your work, we would be t
 	PRIMARYCLASS  = {quant-ph},
 	ARCHIVEPREXIX = {arxiv},
 }
+
+@INPROCEEDINGS{volpe2024predictive,
+	AUTHOR        = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},
+	TITLE         = {{A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem}},
+	YEAR          = {2024},
+	BOOKTITLE     = {IEEE International Conference on Quantum Computing and Engineering (QCE)},
+	EPRINT        = {2408.03613},
+	PRIMARYCLASS  = {quant-ph},
+	ARCHIVEPREXIX = {arxiv},
+}
 ```
 
 ## Acknowledgements

diff --git a/docs/References.rst b/docs/References.rst
@@ -1,6 +1,6 @@
 References
 ==========
 
-If you use *MQT Quantum Auto Optimizer* in your work, we would appreciate if you cited :cite:labelpar:`volpe2024towards`.
+If you use the *MQT Quantum Auto Optimizer* in your work, we would appreciate if you cited :cite:labelpar:`volpe2024towards,volpe2024predictive`.
 
 .. bibliography::
diff --git a/docs/Solver.rst b/docs/Solver.rst
@@ -187,6 +187,36 @@ The class provides for exploiting the solver:
 
 For each of them, the outcome is a Solution object.
 
+Best Solver Prediction:
+-----------------------
+ - solve(
+        self,
+        problem: Problem,
+        num_runs: int = 10,
+        coeff_precision: float = 1.0,
+        token: str = "",
+        max_lambda_update: int = 5,
+        lambda_update_mechanism: str = "sequential penalty increase",
+        lambda_strategy: str = "upper lower bound posiform and negaform method",
+        lambda_value: float = 1.0,
+        save_time: bool = False,
+        save_compilation_time: bool = False,
+    ) -> Solution | bool | None: Solve the problem using the best solver. The parameters are:
+        - *problem*: the problem to solve
+        - *num_runs*: the number of trial
+        - *coeff_precision*: the wanted precision for coefficients (in case of Grover Adaptive Search)
+        - *token*: the token to access the D-Wave API
+        - *max_lambda_update*: the maximum lambda update if the constraints are not satisfied
+        - *lambda_update_mechanism*: the lambda update mechanism among:
+            - *sequential penalty increase*
+            - *scaled sequential penalty increase*
+            - *binary search penalty algorithm*
+        - *lambda_strategy*: for selecting the lambda generation mechanisms
+        - *save_time*: if save the time required for solver execution
+        - *save_compilation_time*: if save the time required for compilation
+
+
+
 
 Examples:
 ---------
@@ -313,6 +343,12 @@ Variational Quantum Eigensolver
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. code-block:: python
 
+    from mqt.qao.constraints import Constraints
+    from mqt.qao.variables import Variables
+    from mqt.qao.objectivefunction import ObjectiveFunction
+    from mqt.qao.problem import Problem
+    from mqt.qao.solver import Solver
+
     variables = Variables()
     constraint = Constraints()
     a0 = variables.add_binary_variable("a")
@@ -328,3 +364,30 @@ Variational Quantum Eigensolver
         problem,
         num_runs=10,
     )
+
+
+Predicted Best Solver
+~~~~~~~~~~~~~~~~~~~~~
+.. code-block:: python
+
+    from mqt.qao.constraints import Constraints
+    from mqt.qao.variables import Variables
+    from mqt.qao.objectivefunction import ObjectiveFunction
+    from mqt.qao.problem import Problem
+    from mqt.qao.solver import Solver
+
+    variables = Variables()
+    constraint = Constraints()
+    a0 = variables.add_binary_variable("a")
+    b0 = variables.add_binary_variable("b")
+    c0 = variables.add_binary_variable("c")
+    cost_function = cast(Expr, -a0 + 2 * b0 - 3 * c0 - 2 * a0 * c0 - 1 * b0 * c0)
+    objective_function = ObjectiveFunction()
+    objective_function.add_objective_function(cost_function)
+    problem = Problem()
+    problem.create_problem(variables, constraint, objective_function)
+    solver = Solver()
+    solution = solver.solve(
+        problem,
+        num_runs=10,
+    )
diff --git a/docs/refs.bib b/docs/refs.bib
@@ -7,3 +7,13 @@ @INPROCEEDINGS{volpe2024towards
 	PRIMARYCLASS  = {quant-ph},
 	ARCHIVEPREXIX = {arxiv},
 }
+
+@INPROCEEDINGS{volpe2024predictive,
+	AUTHOR        = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},
+	TITLE         = {{A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem}},
+	YEAR          = {2024},
+	BOOKTITLE     = {IEEE International Conference on Quantum Computing and Engineering (QCE)},
+	EPRINT        = {2408.03613},
+	PRIMARYCLASS  = {quant-ph},
+	ARCHIVEPREXIX = {arxiv},
+}
diff --git a/noxfile.py b/noxfile.py
@@ -15,7 +15,7 @@
 nox.needs_version = ">=2024.3.2"
 nox.options.default_venv_backend = "uv|virtualenv"
 
-PYTHON_ALL_VERSIONS = ["3.9", "3.10", "3.11", "3.12"]
+PYTHON_ALL_VERSIONS = ["3.9", "3.10", "3.11", "3.12", "3.13"]
 
 BUILD_REQUIREMENTS = [
     "setuptools>=66.1",

diff --git a/pyproject.toml b/pyproject.toml
@@ -28,9 +28,9 @@ dependencies = [
     "qubovert>=1.2.5",
     "matplotlib>=3.8",
     "docplex>=2.21.207",
-    "dwave-system>=1.21.0",
-    "dwave-samplers>=1.2.0",
-    "numpy<2.0.0"
+    "dwave-system>=1.25.0",
+    "dwave-samplers>=1.3.0",
+    "scikit-learn>=1.4.2",
 ]
 
 classifiers = [
@@ -45,6 +45,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Intended Audience :: Science/Research",
     "Natural Language :: English",
     "Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)",
@@ -55,7 +56,7 @@ test = ["pytest>=7.2"]
 coverage = ["mqt.qao[test]", "pytest-cov>=4"]
 docs = [
     "furo>=2023.08.17",
-    "sphinx~=7.0",
+    "sphinx>=7.0",
     "setuptools-scm>=7",
     "sphinxcontrib-bibtex>=2.4.2",
     "sphinx-copybutton",
@@ -130,8 +131,9 @@ module = [
     "qiskit_ibm_runtime.*",
     "matplotlib.*",
     "docplex.*",
+    "dwave.*",
     "dimod.*",
-    "dwave.*"
+    "scikit-learn.*",
 ]
 ignore_missing_imports = true
 
@@ -204,8 +206,6 @@ isort.required-imports = ["from __future__ import annotations"]
 "typing.Self".msg = "Use scikit_build_core._compat.typing.Self instead."
 "typing_extensions.Self".msg = "Use scikit_build_core._compat.typing.Self instead."
 "typing.assert_never".msg = "Use scikit_build_core._compat.typing.assert_never instead."
-"importlib.resources".msg = "Use scikit_build_core._compat.importlib.resources instead."
-"importlib_resources".msg = "Use scikit_build_core._compat.importlib.resources instead."
 
 [tool.ruff.lint.per-file-ignores]
 "tests/**" = ["T20", "ANN"]
@@ -220,3 +220,24 @@ isort.required-imports = ["from __future__ import annotations"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
+
+
+[tool.typos]
+default.extend-ignore-re = [
+    "(?Rm)^.*(#|//)\\s*spellchecker:disable-line$",  # ignore line
+    "(?s)(#|//)\\s*spellchecker:off.*?\\n\\s*(#|//)\\s*spellchecker:on" # ignore block
+]
+default.extend-ignore-identifiers-re = [
+    "BasicAer",
+    "qiskit_aer",
+    "AerSimulator",
+]
+[tool.typos.default.extend-words]
+wille = "wille"
+
+
+[tool.repo-review]
+ignore = [
+    "PC160", # "Uses codespell" -> switched to https://github.com/crate-ci/typos
+    "PC180", # "Uses prettier" -> switched to different prettier-mirror that is not recognized by the check
+]
diff --git a/scripts/linear_regression/LinearRegression.py b/scripts/linear_regression/LinearRegression.py
@@ -19,8 +19,8 @@
 X = np.zeros([N, d])
 Y = np.zeros(N)
 TrainingPercentage = 0.7
-N_Traning = int(N * TrainingPercentage)
-N_Test = N - N_Traning
+N_Training = int(N * TrainingPercentage)
+N_Test = N - N_Training
 i = 0
 for key in df:
     j = 0
@@ -38,21 +38,21 @@
             j += 1
 pca = PCA(n_components=feat)
 X = pca.fit_transform(X=X, y=Y)
-X_traning = np.hstack((X[:N_Traning, :], np.ones((N_Traning, 1))))
-X_test = np.hstack((X[N_Traning:, :], np.ones((N_Test, 1))))
+X_training = np.hstack((X[:N_Training, :], np.ones((N_Training, 1))))
+X_test = np.hstack((X[N_Training:, :], np.ones((N_Test, 1))))
 
-Y_traning = Y[:N_Traning]
-Y_test = Y[N_Traning:]
+Y_training = Y[:N_Training]
+Y_test = Y[N_Training:]
 variables = Variables()
 w = variables.add_continuous_variables_array("w", [feat + 1, 1], -0.25, 0.25, 0.25)
 objective_function = ObjectiveFunction()
 objective_function.add_objective_function(
     cast(
         Expr,
         (
-            np.dot(np.dot(np.dot(np.transpose(w), np.transpose(X_traning)), X_traning), w)
-            - 2 * np.dot(np.dot(np.transpose(w), np.transpose(X_traning)), Y_traning)
-            + np.dot(np.transpose(Y_traning), Y_traning)
+            np.dot(np.dot(np.dot(np.transpose(w), np.transpose(X_training)), X_training), w)
+            - 2 * np.dot(np.dot(np.transpose(w), np.transpose(X_training)), Y_training)
+            + np.dot(np.transpose(Y_training), Y_training)
         )[0, 0],
     )
 )
@@ -73,18 +73,18 @@
     print(solution.best_energy)
     solution.wring_json_reports(filename="simulated_annealing_linear_regression_Iris", problem_features=True)
     w_conf = solution.best_solution["w"]
-    Y_obtained_training = np.dot(X_traning, w_conf)
+    Y_obtained_training = np.dot(X_training, w_conf)
     TP_tr = 0
     TN_tr = 0
     FP_tr = 0
     FN_tr = 0
-    for i in range(N_Traning):
+    for i in range(N_Training):
         if Y_obtained_training[i] > 0:
-            if Y_traning[i] == 1:
+            if Y_training[i] == 1:
                 TP_tr += 1
             else:
                 FP_tr += 1
-        elif Y_traning[i] == 1:
+        elif Y_training[i] == 1:
             FN_tr += 1
         else:
             TN_tr += 1

diff --git a/src/mqt/qao/model/RandomForest/Scaler.pkl b/src/mqt/qao/model/RandomForest/Scaler.pkl
diff --git a/src/mqt/qao/model/RandomForest/ScalerKCross.pkl b/src/mqt/qao/model/RandomForest/ScalerKCross.pkl
diff --git a/src/mqt/qao/model/RandomForest/model.pkl b/src/mqt/qao/model/RandomForest/model.pkl