Reproducibility

MicroQuantum records a reproducibility fingerprint for every execution: a SHA-256 hash over the stable, sorted, JSON-serialized execution configuration — never over object memory addresses.

from microquantum import ExecutionRecord, execution_fingerprint

fp = execution_fingerprint(
    {"plan_name": "ry", "backend": "statevector", "shots": 4096,
     "parameter_bindings": {"theta": 0.5}, "seed": 7}
)
print(fp)                       # 64-hex SHA-256

Configured vs deterministic

• configured_reproducibility — a matching configuration (bindings, backend, shots, seed, SDK version) yields the same fingerprint; the record stays tied to the library that produced it.

• deterministic_execution — whether the backend actually produces identical output for the same configuration. Local simulators with a seed are deterministic; hardware / nondeterministic backends are never claimed bit-for-bit reproducible.

Both live under record.reproducibility:

from microquantum import ExecutionRuntime, Experiment, StatevectorBackend, QuantumCircuit

experiment = Experiment("repro", backend=StatevectorBackend(), shots=16, seed=7)
experiment.add_circuit(QuantumCircuit(1).h(0), name="ghz")
result = experiment.run(ExecutionRuntime(backend=StatevectorBackend()))

for record in result.executions:
    rep = record.reproducibility
    print(rep["fingerprint"])
    print(rep["configured_reproducibility"])   # True
    print(rep["deterministic_execution"])      # None unless a backend asserts determinism

Why configuration-based?

Because the fingerprint is derived from the serialized configuration rather than object identity, records remain comparable across processes, machines and runs — you can verify that “this run used the same parameters” without executing anything. The SDK version is part of the hash so a record stays tied to the exact library that produced it.