Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.

翻译：量子机器学习作为量子计算与经典机器学习的有前景交叉领域，预计将推动计算任务的突破。本文通过系统性文献综述，探讨与量子机器学习相关的安全关切与优势。我们对量子机器学习模型的安全性、量子架构固有的脆弱性以及提出的缓解策略进行了分类与审查。综述揭示，量子机器学习虽具备独特优势，但也引入了经典系统中未曾出现的的新型攻击向量。我们指出了具体风险，例如超导系统中的串扰问题以及离子阱系统中强制重复穿梭操作，这些威胁了量子机器学习的可靠性。然而，对抗训练、量子噪声利用及量子差分隐私等方法在增强量子机器学习鲁棒性方面展现出潜力。我们的综述强调，为确保量子机器学习在现实应用中的安全部署，需持续开展严谨研究。本工作为致力于探索量子机器学习安全方面的研究人员与从业者提供了基础性参考。