A Threat-Model–Driven Robustness Benchmark for Quantum Machine Learning Under Device Noise and Deployment Shift
Year: 2026
Type: conference-paper
Abstract: Quantum machine learning (QML) systems executed on noisy intermediate-scale quantum (NISQ) hardware are vulnerable to both adversarial perturbations and backend-dependent noise effects. Existing robustness evaluations are typically performed under fixed execution conditions and rarely consider deployment shift between training and inference backends. This work proposes a threat-model–driven benchmark framework for evaluating QML robustness under adversarial attacks, stochastic device noise, and backend-dependent deployment variability. Using FGSM and PGD attacks, we analyze robustness scaling under varying training budgets, encoding strategies, and fake quantum backends. Experimental results demonstrate substantial backend-dependent robustness variability and reduced robustness transfer across heterogeneous backend environments. The findings highlight the importance of backend-aware robustness evaluation for realistic NISQ-era QML deployments.
Authors: Volker Reers, Marc Maußner
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204778869