[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

quantum/quantum_kmeans_clustering.py

@@ -4,10 +4,14 @@
 from sklearn.datasets import make_blobs
 from sklearn.preprocessing import MinMaxScaler
 
+
 def generate_data(n_samples=100, n_features=2, n_clusters=2):
-    data, labels = make_blobs(n_samples=n_samples, centers=n_clusters, n_features=n_features, random_state=42)
+    data, labels = make_blobs(
+        n_samples=n_samples, centers=n_clusters, n_features=n_features, random_state=42
+    )
     return MinMaxScaler().fit_transform(data), labels
 
+
 def quantum_distance(point1, point2):
     """
     Quantum circuit explanation:
@@ -19,40 +23,44 @@ def quantum_distance(point1, point2):
     qubit = cirq.LineQubit(0)
     diff = np.clip(np.linalg.norm(point1 - point2), 0, 1)
     theta = 2 * np.arcsin(diff)
-    
-    circuit = cirq.Circuit(
-        cirq.ry(theta)(qubit),
-        cirq.measure(qubit, key='result')
-    )
-    
+
+    circuit = cirq.Circuit(cirq.ry(theta)(qubit), cirq.measure(qubit, key="result"))
+
     result = cirq.Simulator().run(circuit, repetitions=1000)
-    return result.histogram(key='result').get(1, 0) / 1000
+    return result.histogram(key="result").get(1, 0) / 1000
+
 
 def initialize_centroids(data, k):
     return data[np.random.choice(len(data), k, replace=False)]
 
+
 def assign_clusters(data, centroids):
     clusters = [[] for _ in range(len(centroids))]
     for point in data:
-        closest = min(range(len(centroids)), key=lambda i: quantum_distance(point, centroids[i]))
+        closest = min(
+            range(len(centroids)), key=lambda i: quantum_distance(point, centroids[i])
+        )
         clusters[closest].append(point)
     return clusters
 
+
 def recompute_centroids(clusters):
     return np.array([np.mean(cluster, axis=0) for cluster in clusters if cluster])
 
+
 def quantum_kmeans(data, k, max_iters=10):
     centroids = initialize_centroids(data, k)
-    
+
     for _ in range(max_iters):
         clusters = assign_clusters(data, centroids)
         new_centroids = recompute_centroids(clusters)
         if np.allclose(new_centroids, centroids):
             break
         centroids = new_centroids
-    
+
     return centroids, clusters
 
+
 # Main execution
 n_samples, n_clusters = 10, 2
 data, labels = generate_data(n_samples, n_clusters=n_clusters)
@@ -68,12 +76,20 @@ def quantum_kmeans(data, k, max_iters=10):
 plt.subplot(122)
 for i, cluster in enumerate(final_clusters):
     cluster = np.array(cluster)
-    plt.scatter(cluster[:, 0], cluster[:, 1], label=f'Cluster {i+1}')
-plt.scatter(final_centroids[:, 0], final_centroids[:, 1], color='red', marker='x', s=200, linewidths=3, label='Centroids')
+    plt.scatter(cluster[:, 0], cluster[:, 1], label=f"Cluster {i+1}")
+plt.scatter(
+    final_centroids[:, 0],
+    final_centroids[:, 1],
+    color="red",
+    marker="x",
+    s=200,
+    linewidths=3,
+    label="Centroids",
+)
 plt.title("Quantum k-Means Clustering with Cirq")
 plt.legend()
 
 plt.tight_layout()
 plt.show()
 
-print(f"Final Centroids:\n{final_centroids}")
+print(f"Final Centroids:\n{final_centroids}")