⬆️🐍 Lock file maintenance (#386)

This PR contains the following updates:

| Update | Change |
|---|---|
| lockFileMaintenance | All locks refreshed |

🔧 This Pull Request updates lock files to use the latest dependency
versions.

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Author: renovate[bot]

src/mqt/predictor/reward.py

@@ -11,7 +11,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Literal, cast
+from typing import TYPE_CHECKING, Literal
 
 import numpy as np
 from joblib import load
@@ -37,7 +37,7 @@
 def crit_depth(qc: QuantumCircuit, precision: int = 10) -> float:
     """Calculates the critical depth of a given quantum circuit."""
     supermarq_features = calc_supermarq_features(qc)
-    return cast("float", np.round(1 - supermarq_features.critical_depth, precision))
+    return float(np.round(1 - supermarq_features.critical_depth, precision).item())
 
 
 def expected_fidelity(qc: QuantumCircuit, device: Device, precision: int = 10) -> float:
@@ -71,7 +71,7 @@ def expected_fidelity(qc: QuantumCircuit, device: Device, precision: int = 10) -
 
             res *= specific_fidelity
 
-    return cast("float", np.round(res, precision))
+    return float(np.round(res, precision).item())
 
 
 def calc_qubit_index(qargs: list[Qubit], qregs: list[QuantumRegister], index: int) -> int:
@@ -178,7 +178,7 @@ def estimated_success_probability(qc: QuantumCircuit, device: Device, precision:
             second_qubit_idx = calc_qubit_index(qargs, qc.qregs, 1)
             res *= device.get_two_qubit_gate_fidelity(gate_type, first_qubit_idx, second_qubit_idx)
 
-    return cast("float", np.round(res, precision))
+    return float(np.round(res, precision).item())
 
 
 def esp_data_available(device: Device) -> bool:
@@ -260,4 +260,4 @@ def estimated_hellinger_distance(
     feature_vector = calc_device_specific_features(qc, device)
 
     res = model.predict([feature_vector])
-    return cast("float", np.round(res, precision))
+    return float(np.round(res, precision).item())

tests/device_selection/test_predictor_ml.py

@@ -123,18 +123,23 @@ def test_train_random_forest_classifier_and_predict(predictor: ml.Predictor, sou
 
 def test_remove_files(source_path: Path, target_path: Path) -> None:
     """Remove files created during testing."""
-    for file in source_path.iterdir():
-        if file.suffix == ".qasm":
-            file.unlink()
-    for file in target_path.iterdir():
-        if file.suffix == ".qasm":
-            file.unlink()
-    source_path.rmdir()
-    target_path.rmdir()
-
-    for file in (ml.helper.get_path_training_data() / "training_data_aggregated").iterdir():
-        if file.suffix == ".npy":
-            file.unlink()
+    if source_path.exists():
+        for file in source_path.iterdir():
+            if file.suffix == ".qasm":
+                file.unlink()
+        source_path.rmdir()
+
+    if target_path.exists():
+        for file in target_path.iterdir():
+            if file.suffix == ".qasm":
+                file.unlink()
+        target_path.rmdir()
+
+    data_path = ml.helper.get_path_training_data() / "training_data_aggregated"
+    if data_path.exists():
+        for file in data_path.iterdir():
+            if file.suffix == ".npy":
+                file.unlink()
 
 
 def test_predict_device_for_figure_of_merit_no_suitable_device() -> None:

tests/hellinger_distance/test_estimated_hellinger_distance.py

@@ -175,22 +175,29 @@ def test_train_and_qcompile_with_hellinger_model(source_path: Path, target_path:
 
 def test_remove_files(source_path: Path, target_path: Path) -> None:
     """Remove files created during testing."""
-    for file in source_path.iterdir():
-        if file.suffix == ".qasm":
-            file.unlink()
-    for file in target_path.iterdir():
-        if file.suffix == ".qasm":
-            file.unlink()
-    source_path.rmdir()
-    target_path.rmdir()
-
-    for file in (ml.helper.get_path_training_data() / "training_data_aggregated").iterdir():
-        if file.suffix == ".npy":
-            file.unlink()
-
-    for file in (ml.helper.get_path_training_data() / "trained_model").iterdir():
-        if file.suffix == ".joblib":
-            file.unlink()
+    if source_path.exists():
+        for file in source_path.iterdir():
+            if file.suffix == ".qasm":
+                file.unlink()
+        source_path.rmdir()
+
+    if target_path.exists():
+        for file in target_path.iterdir():
+            if file.suffix == ".qasm":
+                file.unlink()
+        target_path.rmdir()
+
+    data_path = ml.helper.get_path_training_data() / "training_data_aggregated"
+    if data_path.exists():
+        for file in data_path.iterdir():
+            if file.suffix == ".npy":
+                file.unlink()
+
+    model_path = ml.helper.get_path_training_data() / "trained_model"
+    if model_path.exists():
+        for file in model_path.iterdir():
+            if file.suffix == ".joblib":
+                file.unlink()
 
 
 def test_predict_device_for_estimated_hellinger_distance_no_device_provided() -> None: