A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models

arXiv:2404.16154v1 Announce Type: cross
Abstract: Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models, it is still largely unknown how to compare adversarial attacks on quantum versus classical models. In this paper, we show how to systematically investigate the similarities and differences in adversarial robustness of classical and quantum models using …

adversarial adversarial attacks analysis area arxiv attacks cs.cr cs.lg industry interest machine machine learning machine learning models qml quant-ph quantum research robustness vulnerable