Towards quantum enhanced adversarial robustness in machine learning

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 in building robust real-world QAML tools. In this Perspective, we discuss recent progress in QAML and identify key challenges. We also suggest future research directions that could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced. To fulfil the potential of quantum machine learning for practical applications in the near future, it needs to be robust against adversarial attacks. West and colleagues give an overview of recent developments in quantum adversarial machine learning, and outline key challenges and future research directions to advance the field.

Automated summary

Machine learning algorithms are powerful but vulnerable to adversarial attacks. Integrating quantum computing with machine learning can improve accuracy and provide better defense against such attacks.