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

2026-05-24

AI Summary

This work addresses two practical threats in quantum machine learning (QML) deployment—hardware calibration drift and adversarial channel replacement—by proposing the QML-PipeGuard framework. It presents the first unified threat model that jointly characterizes natural drift and malicious substitution, specifically tailored for QML systems. The framework introduces a runtime behavioral fingerprint based on an informationally complete set of Pauli observables. Leveraging statistical verification under limited sampling, a tight frame bound (C = √3), and a tolerance decomposition mechanism, QML-PipeGuard enables efficient end-to-end monitoring of QML pipelines. Evaluated on IBM’s Heron r2 processor, the approach accurately detects adversarial channels while tolerating benign calibration drift within a single batch using only approximately 14,000 measurement samples.

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.

Problem

• quantum machine learning
• pipeline integrity
• hardware drift
• adversarial channel substitution
• behavioral fingerprinting

Innovation

• behavioral fingerprinting
• quantum machine learning integrity
• drift-aware monitoring
• adversarial channel detection
• informationally complete observables