Create quantum_metrics.py

quantum/libraries/quantum_metrics.py

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+# quantum_metrics.py
+import numpy as np
+from qiskit.quantum_info import Statevector, DensityMatrix
+
+def fidelity(state1, state2):
+    """
+    Calculate the fidelity between two quantum states.
+    
+    Parameters:
+    - state1: First quantum state (Statevector or DensityMatrix)
+    - state2: Second quantum state (Statevector or DensityMatrix)
+    
+    Returns:
+    - float: Fidelity value between 0 and 1
+    """
+    if isinstance(state1, Statevector) and isinstance(state2, Statevector):
+        return np.abs(np.dot(state1, state2.conjugate()))**2
+    elif isinstance(state1, DensityMatrix) and isinstance(state2, DensityMatrix):
+        return np.trace(np.sqrt(state1.sqrt() @ state2 @ state1.sqrt()))**2
+    else:
+        raise ValueError("Both states must be either Statevector or DensityMatrix.")
+
+def success_probability(counts, target_state):
+    """
+    Calculate the success probability of measuring a target state.
+    
+    Parameters:
+    - counts: Measurement results (dict)
+    - target_state: The target state to measure (str)
+    
+    Returns:
+    - float: Success probability of measuring the target state
+    """
+    total_shots = sum(counts.values())
+    target_count = counts.get(target_state, 0)
+    return target_count / total_shots if total_shots > 0 else 0.0
+
+def average_execution_time(times):
+    """
+    Calculate the average execution time of quantum circuits.
+    
+    Parameters:
+    - times: List of execution times (in seconds)
+    
+    Returns:
+    - float: Average execution time
+    """
+    return np.mean(times) if times else 0.0
+
+def analyze_results(counts, target_state, execution_times):
+    """
+    Analyze the results of a quantum algorithm.
+    
+    Parameters:
+    - counts: Measurement results (dict)
+    - target_state: The target state to measure (str)
+    - execution_times: List of execution times (in seconds)
+    
+    Returns:
+    - dict: Dictionary containing fidelity, success probability, and average execution time
+    """
+    results = {}
+    results['success_probability'] = success_probability(counts, target_state)
+    results['average_execution_time'] = average_execution_time(execution_times)
+    return results
+
+if __name__ == "__main__":
+    # Example usage of the quantum metrics functions
+    # Simulated measurement results
+    counts = {'00': 500, '01': 300, '10': 200, '11': 100}
+    target_state = '00'
+    execution_times = [0.1, 0.15, 0.12, 0.14]  # Example execution times in seconds
+
+    # Analyze results
+    results = analyze_results(counts, target_state, execution_times)
+    print("Analysis Results:")
+    print(f"Success Probability: {results['success_probability']:.2f}")
+    print(f"Average Execution Time: {results['average_execution_time']:.2f} seconds")