Hybrid mapping

noxfile.py

@@ -12,6 +12,7 @@
 
 import argparse
 import os
+import platform
 import shutil
 from typing import TYPE_CHECKING
 
@@ -29,7 +30,7 @@
 
 # TODO(denialhaag): Add 3.14 when all dependencies support it
 #   https://github.com/munich-quantum-toolkit/predictor/issues/420
-PYTHON_ALL_VERSIONS = ["3.10", "3.11", "3.12", "3.13"]
+PYTHON_ALL_VERSIONS = ["3.10", "3.11", "3.12"]
 
 if os.environ.get("CI", None):
     nox.options.error_on_missing_interpreters = True
@@ -66,7 +67,9 @@ def _run_tests(
         "test",
         *install_args,
         "pytest",
+        "-v",
         *pytest_run_args,
+        "--log-cli-level=INFO",
         *session.posargs,
         "--cov-config=pyproject.toml",
         env=env,
@@ -82,6 +85,9 @@ def tests(session: nox.Session) -> None:
 @nox.session(reuse_venv=True, venv_backend="uv", python=PYTHON_ALL_VERSIONS)
 def minimums(session: nox.Session) -> None:
     """Test the minimum versions of dependencies."""
+    if platform.system() == "Windows":
+        session.skip("Too slow on Windows — skipping minimums session.")
+        return
     _run_tests(
         session,
         install_args=["--resolution=lowest-direct"],
@@ -133,5 +139,7 @@ def docs(session: nox.Session) -> None:
         "--frozen",
         "sphinx-autobuild" if serve else "sphinx-build",
         *shared_args,
+        "-v",
+        "--log-cli-level=INFO",
         env=env,
     )

pyproject.toml

@@ -32,7 +32,7 @@ dependencies = [
     "mqt.bench>=2.0.0",
     "qiskit!=1.3.2", # 1.3.2 causes a Qiskit error when using the CommutativeInverseCancellation pass, see https://github.com/Qiskit/qiskit/issues/13742
     "pytket>=1.29.0", # lowest version that supports the used pytket AutoRebase pass instead of auto_rebase
-    "pytket_qiskit>=0.60.0",
+    "pytket_qiskit>=0.71.0",
     "sb3_contrib>=2.0.0",
     "tqdm>=4.66.0",
     "rich>=12.6.0",
@@ -44,8 +44,10 @@ dependencies = [
     "numpy>=1.24; python_version >= '3.11'",
     "numpy>=1.22",
     "numpy>=1.22,<2; sys_platform == 'darwin' and 'x86_64' in platform_machine and python_version < '3.13'",  # Restrict numpy v2 for macOS x86 since it is not supported anymore since torch v2.3.0
-    "torch>=2.7.1,<2.8.0; sys_platform == 'darwin' and 'x86_64' in platform_machine and python_version < '3.13'",  # Restrict torch v2.3.0 for macOS x86 since it is not supported anymore.
+    "torch>=2.7.1,<2.8.0",
     "typing-extensions>=4.1",  # for `assert_never`
+    "qiskit-ibm-transpiler>=0.11.1",
+    "qiskit-ibm-ai-local-transpiler>=0.3.2; python_version < '3.13'"
 ]
 
 classifiers = [
@@ -164,7 +166,7 @@ implicit_reexport = true
 # recent versions of `gym` are typed, but stable-baselines3 pins a very old version of gym.
 # qiskit is not yet marked as typed, but is typed mostly.
 # the other libraries do not have type stubs.
-module = ["qiskit.*", "joblib.*", "sklearn.*", "matplotlib.*", "gymnasium.*", "mqt.bench.*", "sb3_contrib.*", "bqskit.*", "qiskit_ibm_runtime.*", "networkx.*", "stable_baselines3.*"]
+module = ["qiskit.*", "joblib.*", "sklearn.*", "matplotlib.*", "gymnasium.*", "mqt.bench.*", "sb3_contrib.*", "bqskit.*", "qiskit_ibm_runtime.*", "networkx.*", "stable_baselines3.*","qiskit_ibm_transpiler.*"]
 ignore_missing_imports = true
 
 [tool.ruff]
@@ -248,3 +250,8 @@ fom = "fom"
 
 [tool.repo-review]
 ignore = ["GH200"]
+
+[tool.uv]
+override-dependencies = [
+  "networkx==2.8.5",
+]

src/mqt/predictor/ml/predictor.py

@@ -191,7 +191,10 @@ def _compile_all_circuits_devicewise(
             if (path_compiled_circuits / (target_filename + ".qasm")).exists():
                 continue
             try:
-                res = timeout_watcher(rl_compile, [qc, device, self.figure_of_merit, rl_pred], timeout)
+                if sys.platform == "win32":
+                    res = rl_compile(qc, device, self.figure_of_merit, rl_pred)
+                else:
+                    res = timeout_watcher(rl_compile, [qc, device, self.figure_of_merit, rl_pred], timeout)
                 if isinstance(res, tuple):
                     compiled_qc = res[0]
                     with Path(path_compiled_circuits / (target_filename + ".qasm")).open("w", encoding="utf-8") as f:
@@ -206,7 +209,6 @@ def compile_training_circuits(
         path_uncompiled_circuits: Path | None = None,
         path_compiled_circuits: Path | None = None,
         timeout: int = 600,
-        num_workers: int = -1,
     ) -> None:
         """Compiles all circuits in the given directory with the given timeout and saves them in the given directory.
 
@@ -227,19 +229,24 @@ def compile_training_circuits(
             with zipfile.ZipFile(str(path_zip), "r") as zip_ref:
                 zip_ref.extractall(path_uncompiled_circuits)
 
-        Parallel(n_jobs=num_workers, verbose=100)(
-            delayed(self._compile_all_circuits_devicewise)(
-                device, timeout, path_uncompiled_circuits, path_compiled_circuits, logger.level
+        if sys.platform != "win32":
+            Parallel(n_jobs=1, verbose=100)(
+                delayed(self._compile_all_circuits_devicewise)(
+                    device, timeout, path_uncompiled_circuits, path_compiled_circuits, logger.level
+                )
+                for device in self.devices
             )
-            for device in self.devices
-        )
+        else:
+            for device in self.devices:
+                self._compile_all_circuits_devicewise(
+                    device, timeout, path_uncompiled_circuits, path_compiled_circuits, logger.level
+                )
 
     def generate_training_data(
         self,
         path_uncompiled_circuits: Path | None = None,
         path_compiled_circuits: Path | None = None,
         path_training_data: Path | None = None,
-        num_workers: int = -1,
     ) -> None:
         """Creates and saves training data from all generated training samples.
 
@@ -267,7 +274,7 @@ def generate_training_data(
         names_list = []
         scores_list = []
 
-        results = Parallel(n_jobs=num_workers, verbose=100)(
+        results = Parallel(n_jobs=1, verbose=100)(
             delayed(self._generate_training_sample)(
                 filename.name,
                 path_uncompiled_circuits,