对比量子机器学习分布式方法的抗攻击能力差异
Adversarial Robustness in Distributed Quantum Machine Learning
- 区分联邦学习与电路切割等量子分布式范式
- 总结各类分布式方式下模型的抗干扰表现
- 适合关注量子系统安全性的研究者参考
研究量子机器学习（QML）模型的对抗鲁棒性对理解其相对于经典模型的优势及构建可信系统至关重要。分布式QML通过多个量子处理器协作，突破单个设备限制并实现可扩展系统。然而，这种分布可能影响其对抗鲁棒性，使其更易受新型攻击。主要分布式范式包括联邦学习——类似经典方法，在本地数据上训练共享模型并仅传输模型更新；以及量子计算特有的电路分布技术，如电路切割和基于量子隐形传态的方法，这些技术使量子电路可在多设备间分发执行。本文综述了这些分布式方法的差异，总结了每种范式下现有QML模型的对抗鲁棒性研究进展，并探讨该领域的开放问题。
原文摘要 · Abstract (English)
Studying adversarial robustness of quantum machine learning (QML) models is essential in order to understand their potential advantages over classical models and build trustworthy systems. Distributing QML models allows leveraging multiple quantum processors to overcome the limitations of individual devices and build scalable systems. However, this distribution can affect their adversarial robustness, potentially making them more vulnerable to new attacks. Key paradigms in distributed QML include federated learning, which, similar to classical models, involves training a shared model on local data and sending only the model updates, as well as circuit distribution methods inherent to quantum computing, such as circuit cutting and teleportation-based techniques. These quantum-specific methods enable the distributed execution of quantum circuits across multiple devices. This work reviews the differences between these distribution methods, summarizes existing approaches on the adversarial robustness of QML models when distributed using each paradigm, and discusses open questions in this area.
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