QUPID：一种用于智能电网异常检测的分区量子神经网络
QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid
AI总结：
QUPID是一种用于智能电网异常检测的分区量子神经网络，通过量子增强的特征表示和差分隐私技术提升异常检测的准确性和鲁棒性。
摘要：
智能电网基础设施已革新了能源分配，但其日常运营需要强大的异常检测方法来应对与网络物理威胁和系统故障相关的风险，这些故障可能由自然灾害、设备故障和网络攻击引起。传统机器学习（ML）模型在某些领域有效，但它们难以表示智能电网系统中观察到的复杂性。此外，传统ML模型对对抗性操纵高度敏感，使其在现实世界部署中越来越不可靠。量子机器学习（QML）提供了一种独特优势，利用量子增强的特征表示来建模智能电网系统高维性质，同时表现出更强的对抗性操纵抵抗力。在本工作中，我们提出了QUPID，一种分区量子神经网络（PQNN），在异常检测方面优于传统最先进的ML模型。我们扩展了我们的模型到R-QUPID，即使在包含差分隐私（DP）以增强鲁棒性时也能保持性能。此外，我们的分区框架通过高效分配计算工作负载，解决了QML中的一个重大可扩展性问题，使量子增强的异常检测在大规模智能电网环境中变得实用。我们的实验结果在各种场景中证明了QUPID和R-QUPID的有效性，显著提高了异常检测能力和鲁棒性，优于传统ML方法。
英文摘要：
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.
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