The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
MADAR: Efficient continual learning for malware analysis with diversity-aware replay,
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SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.