论文标题
量子机器学习的安全方面：机会，威胁和防御
Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
论文作者
论文摘要
在过去的几年中，量子计算经历了增长突变。量子计算的一种令人兴奋的途径是量子机学习（QML），它可以利用高维的希尔伯特空间来从有限的数据中学习更丰富的表示形式，因此可以有效地解决复杂的学习任务。尽管对QML的兴趣增加了，但并未有很多研究讨论QML的安全方面。在这项工作中，我们探讨了QML在硬件安全域中可能的未来应用。我们还揭示了QML和新兴攻击模型的安全漏洞以及相应的对策。
In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
