Scenario kind pagesEdit on GitHubSource: docs/kinds/by-kind/quantum-anomaly-detect.md

quantum-anomaly-detect (scenario kind)

Scenario kind of the engine v0.38.0, family Inertial, fusion and quantum sensors. Advisory: analysis output from a research and engineering tool, not type-approved navigation equipment and not a safety-of-life service. Every label here is the engine's or the verification matrix's; a kind is MODELLED unless the matrix says otherwise (how a label is earned: the README's Evidence section).

What it does#

MODELLED fault/anomaly detection for quantum PNT systems: a labelled fault catalog (clock frequency-jump/drift/lock-loss; sensor bias-step/dropout), a detection-statistic ROC AUC (with a bootstrap CI from the externally-validated eval_stats) and a minimum-detectable-fault at a fixed false-alarm rate, with the quantum-clock-aided monitor (lower noise) detecting smaller faults — as honest TradeEvidence + representativeness. Gaussian detection-statistic model (AUC = Phi(mu/(sigma*sqrt2))); models the class, illustrative public-source params, no TRL/flight/certification.

One run of scenarios/quantum-anomaly-detect.toml summarises as: quantum-anomaly-detect | AUC quantum 0.9908 vs classical 0.7603 | min-detectable fault quantum 1.312 vs classical 4.372 | 5 fault classes | trade quantum wins 2/2 | MODELLED

Inputs#

From the schema (kshana kinds --json):

  • Required fields: none
  • Optional fields: fault_mu, quantum_sigma, classical_sigma, pfa, pd, samples, seed

The example scenarios/quantum-anomaly-detect.toml sets, among others:

Field Value Unit
classical_sigma 1 not stated
fault_mu 1 not stated
kind quantum-anomaly-detect text
pd 0.9 not stated
pfa 0.001 not stated
quantum_sigma 0.3 not stated
samples 2000 not stated
seed 42 1

Run it#

kshana scenarios/quantum-anomaly-detect.toml

What it gives#

The scalar results of that run, with the units the engine states:

Result Value Unit
classical_auc 0.76025 1
classical_min_detectable 4.371784 1
quantum_auc 0.990789 1
quantum_min_detectable 1.311535 1
trade.frame.engine_version 0.38.0 text
trade.frame.scenario quantum-anomaly-detect text
trade.frame.seed 42 1
trade.representativeness.claim quantum vs classical PNT anomaly detecti text
trade.representativeness.status Modelled text
The chart the engine writes for scenarios/quantum-anomaly-detect.toml, kind quantum-anomaly-detect

Evidence#

No matrix row names this kind by name; the label on its result is the engine's own, above.

The rows the run itself exercised, with the oracle the matrix gives each (the public sources are named there):

  • VALIDATED Analytic binormal ROC AUC = Φ(μ/(σ√2)) and the minimum detectable fault σ·(Φ⁻¹(1−P_fa)+Φ⁻¹(P_d)) underpinning the quantum-fault and anomaly detectors. Oracle (ExternalDataset): scipy 1.17 (Cephes ndtr/ndtri) + scikit-learn roc_auc_score (Pedregosa et al., JMLR 2011), both BSD-3-Clause — independent libraries computing the same uniquely-defined Gaussian-tail / AUC quantities, matched to the A&S-erf floor (~7e-8). The quantum-vs-classical advantage built on top stays MODELLED
  • MODELLED Deterministic, scenario-hashed, SBOM + cross-platform golden gates. Oracle (ExternalDataset): SBOM conformance to the official CycloneDX 1.5 JSON Schema (+ valid SPDX identifiers) — an external published standard, zero validation errors over the full dependency graph; the FoM-determinism / byte-reproducibility part remains a pinned self-consistency check, so the row stays MODELLED
  • PARTNER Not provided — performance models only; cold-atom/clock hardware is a partner's. Oracle (NoneKind): none: a partner-owned discipline, with no module and no test by design
  • MODELLED One reproducible TradeEvidence object (fixed frame: scenario+seed+engine; common per-FoM quantum-vs-classical values with polarity-correct benefit, optional 95%. Oracle (InternalConsistency): Closed-form benefit/winner identities + faithful wrap of the existing quantum_trade::TradeResult; honesty tied to the representativeness ledger and verification labels
  • MODELLED Labelled quantum-fault catalog (clock frequency-jump/drift/lock-loss; sensor bias-step/dropout), a detection-statistic ROC AUC with a bootstrap CI, and a minimu. Oracle (InternalConsistency): Closed-form Gaussian AUC = Phi(mu/(sigma*sqrt2)) cross-checked against the externally-validated eval_stats::bootstrap_auc_ci (vs scikit-learn) + detection analytic thresholds

Limitations#

What the run's own report says is not modelled or not validated:

  • MODELLED fault/anomaly detection for quantum PNT systems: a labelled fault catalog (clock frequency-jump/drift/lock-loss; sensor bias-step/dropout), a detection-statistic ROC AUC (with a bootstrap CI from the externally-validated eval_stats) and a minimum-detectable-fault at a fixed false-alarm rate, with the quantum-clock-aided monitor (lower noise) detecting smaller faults — as honest TradeEvidence + representative (kind catalogue (kshana kinds --json))
  • Fault/anomaly detection for quantum PNT systems — labelled fault catalog + ROC AUC + minimum-detectable fault, quantum-clock-aided vs classical monitor. MODELLED; illustrative public-source params; models the class, no TRL/flight claim. (scenario file comment, lines 1 to 3)
  • Reproducibility & software assurance is MODELLED, not validated: a sub-claim is externally checked, but the whole capability composes modelled pieces, so the capability stays Modelled. (verification matrix (docs/MODELLED-RATIONALE.md))
  • Quantum-vs-classical trade evidence (common shape) is MODELLED, not validated: checked against its own closed-form / analytic identity — catches transcription and coefficient errors, but is not an external oracle. (verification matrix (docs/MODELLED-RATIONALE.md))
  • Fault/anomaly detection for quantum PNT systems is MODELLED, not validated: checked against its own closed-form / analytic identity — catches transcription and coefficient errors, but is not an external oracle. (verification matrix (docs/MODELLED-RATIONALE.md))

See also#