[论文解读] QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid

Hoang M. Ngo, Tre’ R. Jeter+2 more|arXiv (Cornell University)|Jan 16, 2026

Smart Grid Security and Resilience被引用 0

一句话总结

论文提出了 QUPID，一种用于智能电网异常检测的分区量子神经网络，鲁棒变体 R-QUPID 通过量子诱发噪声和差分隐私来增强鲁棒性和可扩展性。

ABSTRACT

Smart grid infrastructures have revolutionized energy distribution, but their day-to-day operations require robust anomaly detection methods to counter risks associated with cyber-physical threats and system faults potentially caused by natural disasters, equipment malfunctions, and cyber attacks. Conventional machine learning (ML) models are effective in several domains, yet they struggle to represent the complexities observed in smart grid systems. Furthermore, traditional ML models are highly susceptible to adversarial manipulations, making them increasingly unreliable for real-world deployment. Quantum ML (QML) provides a unique advantage, utilizing quantum-enhanced feature representations to model the intricacies of the high-dimensional nature of smart grid systems while demonstrating greater resilience to adversarial manipulation. In this work, we propose QUPID, a partitioned quantum neural network (PQNN) that outperforms traditional state-of-the-art ML models in anomaly detection. We extend our model to R-QUPID that even maintains its performance when including differential privacy (DP) for enhanced robustness. Moreover, our partitioning framework addresses a significant scalability problem in QML by efficiently distributing computational workloads, making quantum-enhanced anomaly detection practical in large-scale smart grid environments. Our experimental results across various scenarios exemplifies the efficacy of QUPID and R-QUPID to significantly improve anomaly detection capabilities and robustness compared to traditional ML approaches.

研究动机与目标

• 解决智能电网中对鲁棒、实时异常检测的需求。
• 利用量子机器学习对高维、复杂的电网数据进行建模。
• 引入分区方案以降低量子比特数量并提升可扩展性。
• 通过量子编码实现对复数数据的处理。
• 提供通过量子诱导噪声与差分隐私实现的鲁棒性框架。

提出的方法

• 按相邻的测量设备（PMU）对输入数据进行分区，形成 K 个分区。
• 使用振幅编码将复数 PMU 测量量编码到量子态中。
• 对每个分区使用一个小型带参数量子电路（PQC）处理，产生潜在特征 h^(k)。
• 将分区特征拼接形成全局潜在向量 h，并通过两层稠密 ReLU 层获得 logit。
• 使用交叉熵损失和基于梯度的优化进行训练。
• 通过在 PQC 之后引入去极化噪声并结合经典噪声与差分隐私，扩展为 R-QUPID，从而获得可证实的鲁棒性。

实验结果

研究问题

• RQ1 QUPID 在多场景下的智能电网异常分类上是否优于最先进的经典与混合基线？
• RQ2 分区方法是否提升量子增强异常检测的可扩展性和训练效率？
• RQ3 量子诱导噪声是否能放大差分隐私保证并对抗对抗性攻击提供可证实的鲁棒性？
• RQ4 在噪声量子-经典混合环境中，R-QUPID 的理论保证和实际性能优势是什么？

主要发现

• QUPID 与 R-QUPID 在 15 个 ICS 数据集场景与 7 项指标上持续优于五个最先进基线。
• 分区框架降低了量子比特需求并提升了训练效率，同时不牺牲准确性。
• 在量子空间中的复数数据编码相较于实数基线，提升了对智能电网关系的表示能力。
• 量子诱导噪声可以放大 DP（差分隐私）保证，贡献于对抗性鲁棒性的理论证明。
• R-QUPID 在混合量子-经典模型中提供对对抗威胁的可证明鲁棒性保障。