Create quantum_svm.py

quantum_integration/quantum_machine_learning/quantum_svm.py

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+from qiskit import Aer
+from qiskit_machine_learning.algorithms import QSVC
+from qiskit_machine_learning.kernels import QuantumKernel
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+def quantum_svm(training_data, training_labels):
+    # Create a quantum kernel
+    quantum_kernel = QuantumKernel(quantum_instance=Aer.get_backend('aer_simulator'))
+
+    # Create a QSVC model
+    model = QSVC(quantum_kernel=quantum_kernel)
+
+    # Fit the model
+    model.fit(training_data, training_labels)
+
+    # Predict using the model
+    predictions = model.predict(training_data)
+
+    return predictions
+
+if __name__ == "__main__":
+    # Generate synthetic data for classification
+    X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train the quantum SVM
+    predictions = quantum_svm(X_train, y_train)
+
+    # Evaluate the model
+    accuracy = accuracy_score(y_train, predictions)
+    print("Training Accuracy:", accuracy)