Universal adversarial perturbations for multiple classification tasks with quantum classifiers

Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples...

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Veröffentlicht in:	Machine learning: science and technology 2023-12, Vol.4 (4), p.45009
1. Verfasser:	Qiu, Yun-Zhong
Format:	Artikel
Sprache:	eng
Schlagworte:	adversarial machine learning Classification Classifiers Machine learning Perturbation quantum computing quantum continual learning
Online-Zugang:	Volltext
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