Towards quantum enhanced adversarial robustness in machine learning
This is an incremental review that identifies challenges and future directions for QAML, potentially benefiting researchers in quantum computing and machine learning security.
The paper reviews quantum adversarial machine learning (QAML), which tackles the vulnerability of machine learning algorithms to adversarial attacks by leveraging quantum computing for enhanced robustness, though it notes challenges remain for real-world application.
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.