🚨 fixed ruff errors

tests/compilation/test_predictor_rl.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import re
 from pathlib import Path
 
 import pytest
@@ -23,7 +24,7 @@ def test_predictor_env_reset_from_string() -> None:
 
 def test_predictor_env_esp_error() -> None:
     """Test the predictor environment with ESP as figure of merit and missing calibration data."""
-    with pytest.raises(ValueError, match="Missing calibration data for ESP calculation on ibm_montreal."):
+    with pytest.raises(ValueError, match=re.escape("Missing calibration data for ESP calculation on ibm_montreal.")):
         rl.Predictor(figure_of_merit="estimated_success_probability", device_name="ibm_montreal")
 
 
@@ -42,7 +43,8 @@ def test_qcompile_with_newly_trained_models() -> None:
     model_path = Path(rl.helper.get_path_trained_model() / (model_name + ".zip"))
     if not model_path.exists():
         with pytest.raises(
-            FileNotFoundError, match="The RL model is not trained yet. Please train the model before using it."
+            FileNotFoundError,
+            match=re.escape("The RL model is not trained yet. Please train the model before using it."),
         ):
             rl.qcompile(qc, figure_of_merit=figure_of_merit, device_name=device)
 
@@ -61,7 +63,7 @@ def test_qcompile_with_newly_trained_models() -> None:
 def test_qcompile_with_false_input() -> None:
     """Test the qcompile function with false input."""
     qc = get_benchmark("dj", 1, 5)
-    with pytest.raises(ValueError, match="figure_of_merit must not be None if predictor_singleton is None."):
+    with pytest.raises(ValueError, match=re.escape("figure_of_merit must not be None if predictor_singleton is None.")):
         rl.helper.qcompile(qc, None, "quantinuum_h2")
-    with pytest.raises(ValueError, match="device_name must not be None if predictor_singleton is None."):
+    with pytest.raises(ValueError, match=re.escape("device_name must not be None if predictor_singleton is None.")):
         rl.helper.qcompile(qc, "expected_fidelity", None)

tests/device_selection/test_helper_ml.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import re
+
 import pytest
 
 from mqt.bench import benchmark_generator
@@ -10,7 +12,9 @@
 
 def test_load_and_save_training_data() -> None:
     """Test the loading and saving of the training data."""
-    with pytest.raises(FileNotFoundError, match="Training data not found. Please run the training script first."):
+    with pytest.raises(
+        FileNotFoundError, match=re.escape("Training data not found. Please run the training script first.")
+    ):
         ml.helper.load_training_data("false_input")  # type: ignore[arg-type]
 
     training_data, names_list, scores_list = ml.helper.load_training_data()

tests/device_selection/test_predictor_ml.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import re
 import sys
 from pathlib import Path
 from typing import Literal
@@ -39,7 +40,7 @@ def test_train_and_predictor_random_forest_classifier() -> None:
         assert 0 <= elem <= 1
 
     with pytest.raises(
-        FileNotFoundError, match="The ML model is not trained yet. Please train the model before using it."
+        FileNotFoundError, match=re.escape("The ML model is not trained yet. Please train the model before using it.")
     ):
         ml.helper.predict_device_for_figure_of_merit(qc, "false_input")  # type: ignore[arg-type]
 
@@ -99,7 +100,7 @@ def test_compile_all_circuits_for_dev_and_fom() -> None:
         dump(qc, f)
 
     if sys.platform == "win32":
-        with pytest.warns(RuntimeWarning, match="Timeout is not supported on Windows."):
+        with pytest.warns(RuntimeWarning, match=re.escape("Timeout is not supported on Windows.")):
             predictor.compile_all_circuits_devicewise(
                 device_name="ionq_harmony",
                 timeout=100,