QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity

• URL: http://arxiv.org/abs/2605.25066v1
• Date: Sun, 24 May 2026 13:21:41 GMT
• Title: QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
• Abstract summary: Integrity of quantum machine learning is a practical concern in regulated industries.<n>We introduce QML-PipeGuard, a contract-based framework addressing both concerns.<n>We validate the framework end-to-end on a two-qubitVM pipeline on the IBM Heron r2 processor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) is moving from research prototypes to deployed cloud services. As QML enters regulated industries, the integrity of the quantum stage becomes a practical concern on two fronts: noisy hardware drifts at the channel level between recalibrations, and an adversary with control over the execution environment can substitute the declared quantum channel with a behaviorally similar but mathematically distinct one. Neither concern is covered by existing QML verification work on pulse-level noise, input drift, input-perturbation robustness, or device identity. We introduce QML-PipeGuard, a contract-based framework addressing both concerns under a single mathematical machinery. It characterizes a QML pipeline at runtime by its behavioral fingerprint, the vector of observable expectation values under a tomographically structured measurement family, and operates in two modes: drift-aware monitoring that absorbs benign calibration changes within a calibrated tolerance, and adversarial detection that catches channel substitution as a violation of an informationally complete observable contract. The framework contributes a pipeline-composition treatment of the encoder-ansatz-measurement channel with a QML-specific threat model (tight frame-bound C=sqrt(3) for the single-qubit Pauli family), a finite-shot sample-complexity bound, and a tolerance decomposition separating adversarial and natural-drift contributions. We validate the framework end-to-end on a two-qubit QSVM pipeline on the IBM Heron r2 processor (ibm_fez), with a sample-complexity validation on a noise-matched simulator. The prescribed measurement budget (about 1.4e4 shots) fits in a single batched job, the sneaky channel is detected with a wide safety margin while evading the weak contract, and the typical hardware drift sits within tolerance.

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