# 3. Measure in X basis to check superposition integrity for q in qubits: circuit.h(q) circuit.measure_all()
# 5. Compute crack score expected_parity = 0 # for GHZ, expect even parity fidelity = measure_parity_fidelity(counts, expected_parity) crack_score = 1 - fidelity
# 4. Execute job = backend.run(circuit, shots=1024) counts = job.result().get_counts()
return { "superposition_fidelity": fidelity, "crack_score": crack_score, "crack_location": detect_outliers(counts, qubits), "raw_counts": counts } { "superposition_fidelity": 0.82, "crack_score": 0.18, "crack_location": [2], "benchmark_report": { "basis": "X", "expected_parity": 0, "measured_parity_0": 0.82, "measured_parity_1": 0.18, "threshold_crack": 0.15, "status": "minor_crack_detected" } } If you meant a different kind of “crack” (e.g., structural crack detection using superposition of ultrasonic waves, or a software benchmark for “superposition” in a simulation framework), please clarify the domain (quantum hardware, NISQ benchmarking, materials science, or classical signal processing). I can then provide the exact feature specification, code, or math model you need.
It looks like you’re asking for a to benchmark superposition in the context of a crack — possibly in a quantum computing, signal processing, or materials modeling scenario.
To give you a useful answer, I’ll assume you mean a that detects or quantifies “cracks” (decoherence, noise, or errors) in a quantum system.