Towards quantum enhanced adversarial robustness in machine learning

TL;DR

This paper reviews recent progress in quantum adversarial machine learning, highlighting potential advantages in robustness and efficiency, while discussing challenges and future directions for practical implementation.

Contribution

It provides a comprehensive overview of recent advances in quantum adversarial machine learning and outlines key challenges and future research directions.

Findings

Quantum approaches show promise for improved robustness against adversarial attacks.

Early results indicate potential quantum advantage in adversarial robustness.

Identifies key challenges in scaling and noise reduction for practical QAML applications.

Abstract

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 reduced.