Related papers: Quantum Adversarial Learning for Kernel Methods

Quantum Adversarial Learning for Kernel Methods

URL: http://arxiv.org/abs/2404.05824v1
Date: Mon, 8 Apr 2024 19:23:17 GMT
Title: Quantum Adversarial Learning for Kernel Methods
Authors: Giuseppe Montalbano, Leonardo Banchi,
Abstract summary: We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable to adversarial attacks. Simple defence strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defence strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.

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