QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid

TL;DR

This paper introduces QUPID, a partitioned quantum neural network designed for anomaly detection in smart grids, demonstrating improved accuracy and robustness over classical models, especially when incorporating differential privacy.

Contribution

The paper presents a novel partitioned quantum neural network architecture, QUPID, tailored for smart grid anomaly detection, and extends it to R-QUPID with differential privacy for enhanced robustness and scalability.

Findings

QUPID outperforms classical ML models in anomaly detection accuracy.

R-QUPID maintains performance with differential privacy, enhancing robustness.

Partitioning framework improves scalability for large-scale smart grid applications.

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.