为量子机器学习设计安全评估新方法，用量子距离度量检测模型异常。
Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics
- 基于量子态空间距离度量，实现对QML模型的后分类评估。
- 在QCNN和VQC模型上验证，能有效识别概念漂移问题。
- 适合关注量子模型安全与可解释性的研究人员使用。
随着量子计算的发展，量子机器学习（QML）在安全关键系统中的应用日益广泛。然而，传统经典机器学习的安全监控方法因量子计算的本质差异而无法直接适用。本文提出Q-SafeML，一种针对QML的安全监控方法。该方法基于近期的SafeML，利用统计距离度量评估模型准确性并提供推理置信度，但进行了关键改进：引入量子中心的距离度量，以匹配QML输出的概率特性。这一转变使评估从依赖数据集、不依赖分类器的范式，转变为模型相关的后分类评估。其核心在于量子系统特有的表征约束，要求在量子态空间上定义距离度量。Q-SafeML通过检测运行数据与训练数据之间的距离，识别概念漂移。在QCNN和VQC模型上的实验表明，该方法支持知情的人工干预，提升系统的透明度与安全性。
原文摘要 · Abstract (English)
The rise of machine learning in safety-critical systems has paralleled advancements in quantum computing, leading to the emerging field of Quantum Machine Learning (QML). While safety monitoring has progressed in classical ML, existing methods are not directly applicable to QML due to fundamental differences in quantum computation. Given the novelty of QML, dedicated safety mechanisms remain underdeveloped. This paper introduces Q-SafeML, a safety monitoring approach for QML. The method builds on SafeML, a recent method that utilizes statistical distance measures to assess model accuracy and provide confidence in the reasoning of an algorithm. An adapted version of Q-SafeML incorporates quantum-centric distance measures, aligning with the probabilistic nature of QML outputs. This shift to a model-dependent, post-classification evaluation represents a key departure from classical SafeML, which is dataset-driven and classifier-agnostic. The distinction is motivated by the unique representational constraints of quantum systems, requiring distance metrics defined over quantum state spaces. Q-SafeML detects distances between operational and training data addressing the concept drifts in the context of QML. Experiments on QCNN and VQC Models show that this enables informed human oversight, enhancing system transparency and safety.
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