Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era

• URL: http://arxiv.org/abs/2511.02602v1
• Date: Tue, 04 Nov 2025 14:24:17 GMT
• Title: Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
• Authors: Ferhat Ozgur Catak, Jungwon Seo, Umit Cali,
• Abstract summary: This research offers a broad roadmap for Trustworthy Quantum Machine Learning (TQML)<n>It integrates three foundational pillars of reliability: (i) uncertainty quantification for calibrated and risk-aware decision making, (ii) robustness adversarial against classical and quantum-native threat models, and (iii) privacy preservation in distributed and delegated quantum learning scenarios.<n>This roadmap seeks to define trustworthiness as a first-class design objective for quantum AI.
• Score: 3.1351527202068445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent reliable deployment of QML in real-world, safety-critical settings. This research offers a broad roadmap for Trustworthy Quantum Machine Learning (TQML), integrating three foundational pillars of reliability: (i) uncertainty quantification for calibrated and risk-aware decision making, (ii) adversarial robustness against classical and quantum-native threat models, and (iii) privacy preservation in distributed and delegated quantum learning scenarios. We formalize quantum-specific trust metrics grounded in quantum information theory, including a variance-based decomposition of predictive uncertainty, trace-distance-bounded robustness, and differential privacy for hybrid learning channels. To demonstrate feasibility on current NISQ devices, we validate a unified trust assessment pipeline on parameterized quantum classifiers, uncovering correlations between uncertainty and prediction risk, an asymmetry in attack vulnerability between classical and quantum state perturbations, and privacy-utility trade-offs driven by shot noise and quantum channel noise. This roadmap seeks to define trustworthiness as a first-class design objective for quantum AI.

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