Submit your quantum circuit. Receive a formal, multi-criterion certification report with cryptographic audit trail. Know whether your computation is trustworthy before you act on it.
Start CertificationPaste a PennyLane circuit function or upload a Python file. Define your quantum computation using standard gates and measurements.
Choose which certification criteria to test against — from CPTP preservation and spectral stability to formal model checking and audit integrity.
Receive a pass/fail verdict for each criterion, diagnostic values, and a blockchain-anchored audit hash proving exactly what was tested and when.
Submit a circuit to generate a certification report.
Each criterion enforces a specific mathematical invariant. If any criterion fails, the computation has produced output that cannot be trusted for decision-making.
Quantum state evolution must preserve complete positivity and trace. Density matrices must remain physical throughout the entire computation.
The spectral radius of the propagation operator must remain below unity, ensuring that numerical errors contract rather than amplify over time.
Internal monitoring metrics must produce well-calibrated, bounded scores that reliably flag when the computation has departed from expected behavior.
Dissipative dynamics must actually dissipate. Entropy must converge to equilibrium as thermodynamics requires — no artificial violations of the second law.
Tensor compression must introduce only bounded, controlled approximation error, with the bound provably satisfied by the discarded singular values.
Probabilistic model checking must confirm that the system maintains operational stability with high probability over extended computation horizons.
Relative entropy must decay exponentially at a rate consistent with the system's Log-Sobolev constant — no artificial slowdowns or accelerations.
The full computation history is logged to a cryptographic hash chain. Any tampering with any record in the chain is detectable and provable.
Training with differential privacy guarantees ensures no information leakage about individual training examples — required for sensitive and classified workloads.