Towards quantum enhanced adversarial robustness in machine learning. (arXiv:2306.12688v1 [quant-ph])

Towards quantum enhanced adversarial robustness in machine learning. (arXiv:2306.12688v1 [quant-ph])
By: <a href="">Maxwell T. West</a>, <a href="">Shu-Lok Tsang</a>, <a href="">Jia S. Low</a>, <a href="">Charles D. Hill</a>, <a href="">Christopher Leckie</a>, <a href="">Lloyd C.L. Hollenberg</a>, <a href="">Sarah M. Erfani</a>, <a href="">Muhammad Usman</a> Posted: June 23, 2023

Machine learning algorithms are powerful tools for data driven tasks such as
image classification and feature detection, however their vulnerability to
adversarial examples – input samples manipulated to fool the algorithm –
remains a serious challenge. The integration of machine learning with quantum
computing has the potential to yield tools offering not only better accuracy
and computational efficiency, but also superior robustness against adversarial
attacks. Indeed, recent work has employed quantum mechanical phenomena to
defend against adversarial attacks, spurring the rapid development of the field
of quantum adversarial machine learning (QAML) and potentially yielding a new
source of quantum advantage. Despite promising early results, there remain
challenges towards building robust real-world QAML tools. In this review we
discuss recent progress in QAML and identify key challenges. We also suggest
future research directions which could determine the route to practicality for
QAML approaches as quantum computing hardware scales up and noise levels are

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