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.

翻译：机器学习算法在图像分类和特征检测等数据驱动任务中展现出强大能力，但其对对抗样本（即旨在欺骗算法的输入样本）的脆弱性仍是严峻挑战。机器学习与量子计算的融合有望催生不仅具有更高精度和计算效率，且对对抗攻击具有更强鲁棒性的工具。事实上，近期研究已利用量子力学现象防御对抗攻击，推动了量子对抗机器学习（QAML）领域的快速发展，并可能催生量子优势的新来源。尽管初步结果令人鼓舞，但在构建实用的鲁棒性QAML工具方面仍面临挑战。本综述讨论了QAML的最新进展，识别了关键挑战，并提出了未来研究方向——这些方向将决定随着量子计算硬件规模扩大与噪声水平降低，QAML方法走向实用化的路径。