Add Quantum k-Means Clustering Implementation

quantum/quantum_kmeans_clustering.py

@@ -0,0 +1,105 @@
+import cirq
+import numpy as np
+import matplotlib.pyplot as plt
+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)
+    return MinMaxScaler().fit_transform(data), labels
+
+def quantum_distance(point1, point2):
+    """
+    Computes the quantum distance between two points.
+
+    :param point1: First point as a numpy array.
+    :param point2: Second point as a numpy array.
+    :return: Quantum distance between the two points.
+
+    >>> point_a = np.array([1.0, 2.0])
+    >>> point_b = np.array([1.5, 2.5])
+    >>> result = quantum_distance(point_a, point_b)
+    >>> assert isinstance(result, float)
+    """
+    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"))
+
+    result = cirq.Simulator().run(circuit, repetitions=1000)
+    return result.histogram(key="result").get(1, 0) / 1000
+
+
+def initialize_centroids(data: np.ndarray, k: int) -> np.ndarray:
+    """
+    Initializes centroids for k-means clustering.
+
+    :param data: The dataset from which to initialize centroids.
+    :param k: The number of centroids to initialize.
+    :return: An array of initialized centroids.
+
+    >>> data = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> centroids = initialize_centroids(data, 2)
+    >>> assert centroids.shape == (2, 2)
+    """
+    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])
+        )
+        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)
+
+plt.figure(figsize=(12, 5))
+
+plt.subplot(121)
+plt.scatter(data[:, 0], data[:, 1], c=labels)
+plt.title("Generated Data")
+
+final_centroids, final_clusters = quantum_kmeans(data, n_clusters)
+
+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.title("Quantum k-Means Clustering with Cirq")
+plt.legend()
+
+plt.tight_layout()
+plt.show()
+
+print(f"Final Centroids:\n{final_centroids}")