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

TL;DR

This paper proposes a unified roadmap for Trustworthy Quantum Machine Learning (TQML) in the NISQ era, centering reliability on three pillars: uncertainty quantification, adversarial robustness, and privacy preservation. It develops a quantum-information-theoretic framework for trust metrics, outlines quantum-specific attack surfaces and defenses, and demonstrates feasibility on current devices through a unified trust-assessment pipeline. The work combines theoretical foundations with extensive experiments on uncertainty quantification, adversarial attacks including FGSM and PGD, and federated quantum learning with differential privacy to reveal practical trade-offs between reliability, robustness, and privacy. The findings show that uncertainty metrics effectively predict misclassifications, classical gradient-based attacks pose significant threats to quantum classifiers (with quantum-state perturbations being less effective), and privacy-preserving federated learning can achieve meaningful privacy gains with manageable accuracy costs. Overall, the roadmap provides concrete methods, metrics, and experimental validation to advance trustworthy quantum AI during the NISQ era and beyond, urging standardized benchmarks and certification pathways for responsible quantum deployment.

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.

Figures (15)

• Figure 1: Uncertainty sources in a supervised QML pipeline. Classical inputs are encoded into quantum states via a feature map, processed by a parameterized quantum circuit (PQC), and measured in a chosen basis before classical post-processing. Predictive uncertainty arises from (i) aleatoric uncertainty due to data ambiguity, (ii) epistemic uncertainty from limited knowledge of PQC parameters, and (iii) technical uncertainty due to device noise and finite sampling (shot noise).
• Figure 2: Uncertainty quantification methods in QML. The diagram organizes predictive uncertainty into three primary sources: aleatoric (data-related), epistemic (model-related), and technical (hardware-related). Each source is linked to representative estimation strategies (e.g., shot-based sampling, Bayesian variational circuits, error mitigation), evaluation metrics (e.g., predictive entropy, mutual information, credible intervals), and downstream decision-making tools (e.g., selective prediction, active data acquisition, shot allocation). The framework highlights how uncertainty analysis in QML is not only diagnostic but also prescriptive, shaping both model development and deployment.
• Figure 3: Privacy threat models in hybrid quantum--classical QML. T1: centralized training risks (server or aggregation compromise); T2: distributed training risks (updates or gradients leaking private data); T3: hybrid communication risks (interception across quantum--classical channels); T4: delegated computation risks (untrusted quantum providers).
• Figure 4: Distribution of predictive entropy for correct vs. incorrect predictions. Kernel density estimation (KDE) curves show clear bimodal separation: misclassified samples concentrate at high entropy ($\mu_{\text{incorrect}} = 0.900$), while correct predictions span a broader range with lower mean ($\mu_{\text{correct}} = 0.475$). The statistical test confirms this difference is highly significant ($t = 11.383, p < 0.0001$), validating entropy as a reliable proxy for prediction correctness. Dashed vertical lines indicate distribution means.
• Figure 5: Spatial distribution of predictive uncertainty across feature space for varying shot counts. Orange triangles denote the 10 samples with the highest entropy (high uncertainty); blue squares indicate the 10 samples with the lowest entropy (high confidence). Arrows connect samples to annotation boxes. High-uncertainty samples consistently localize along the non-linear decision boundary, while confident predictions occur in class-dense regions. As shot count increases from 50 to 1000, the spatial pattern stabilizes, demonstrating a reduction in technical (shot noise) uncertainty while preserving the underlying epistemic and aleatoric structure.