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Effectiveness of Adversarial Training Across Quantum ML Architectures and Datasets

Determine the effectiveness of adversarial training as a defense mechanism for quantum machine learning models across diverse architectures (e.g., quantum variational circuits) and datasets by conducting systematic evaluations under a range of adversarial attack methods and settings.

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Background

While some studies show improvements from adversarial training for quantum classifiers, others report limited gains and suggest possible inherent robustness in quantum models. The disparate findings arise from differences in datasets, number of classes, and attack types.

The authors call for a systematic investigation to understand the impact and limitations of adversarial training in quantum settings, indicating that its general effectiveness across architectures and datasets is not yet established.

References

Although early research supports the use of AT as a defense mechanism in QML, its overall effectiveness across architectures and datasets remains an open research question.

— Adversarially Robust Quantum Transfer Learning (2510.16301 - Khatun et al., 18 Oct 2025) in Section 2 (Literature Review), Subsection “Quantum Adversarial Training”