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.

翻译：智能电网基础设施彻底改变了能源分配方式，但其日常运行需要稳健的异常检测方法，以应对网络物理威胁和系统故障带来的风险，这些风险可能源于自然灾害、设备故障和网络攻击。传统机器学习模型在多个领域表现有效，但难以表征智能电网系统中观察到的复杂性。此外，传统机器学习模型极易受到对抗性操纵，使其在实际部署中的可靠性日益降低。量子机器学习具有独特优势，它利用量子增强的特征表示来建模智能电网系统高维特性的复杂性，同时展现出更强的对抗性操纵鲁棒性。本文提出QUPID，一种分区量子神经网络，其在异常检测任务中超越了传统最先进的机器学习模型。我们进一步扩展模型至R-QUPID，该模型即使在引入差分隐私以增强鲁棒性的情况下仍能保持性能。此外，我们的分区框架通过高效分配计算负载，解决了量子机器学习中一个重要的可扩展性问题，使得量子增强的异常检测能够实际应用于大规模智能电网环境。我们在多种场景下的实验结果表明，与传统机器学习方法相比，QUPID和R-QUPID能显著提升异常检测能力与鲁棒性。