Predominant Aspects on Security for Quantum Machine Learning: Literature Review
CoRR(2024)
摘要
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. Techniques 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要