diff --git a/anomaly_detection/quantum_anomaly_detection.py b/anomaly_detection/quantum_anomaly_detection.py
new file mode 100644
index 0000000..bcd8e71
--- /dev/null
+++ b/anomaly_detection/quantum_anomaly_detection.py
@@ -0,0 +1,47 @@
+# quantum_anomaly_detection.py
+
+import numpy as np
+import qiskit as qk
+from qiskit.visualization import plot_histogram, plot_bloch_vector
+from qiskit.circuit.library import QFT
+from qiskit.aqua.algorithms import QSVM
+from qiskit.aqua.components.feature_maps import SecondOrderExpansion
+from qiskit.aqua.components.variational_forms import RealAmplitudes
+from qiskit.aqua.utils.supporting import minimize
+
+def create_quantum_anomaly_detection_model(training_data, testing_data, quantum_instance):
+    # Define the feature map
+    feature_map = SecondOrderExpansion(feature_dimension=training_data.shape[1], reps=1)
+
+    # Define the variational form
+    variational_form = RealAmplitudes(feature_map.num_qubits, reps=1)
+
+    # Create the QSVM model
+    qsvm = QSVM(feature_map=feature_map, variational_form=variational_form, quantum_instance=quantum_instance)
+
+    # Train the QSVM model
+    qsvm.train(training_data)
+
+    # Evaluate the QSVM model
+    qsvm_predictions = qsvm.predict(testing_data)
+
+    return qsvm, qsvm_predictions
+
+def run_quantum_anomaly_detection(training_data, testing_data, quantum_instance):
+    qsvm, qsvm_predictions = create_quantum_anomaly_detection_model(training_data, testing_data, quantum_instance)
+
+    # Calculate the accuracy of the QSVM model
+    accuracy = np.mean(qsvm_predictions == testing_data.argmax(axis=1))
+
+    print(f"Quantum Anomaly Detection Accuracy: {accuracy}")
+
+if __name__ == "__main__":
+    # Load your training and testing data here
+    training_data = ...
+    testing_data = ...
+
+    # Create a quantum instance
+    quantum_instance = qk.Aer.get_backend("qasm_simulator")
+
+    # Run the quantum anomaly detection
+    run_quantum_anomaly_detection(training_data, testing_data, quantum_instance)