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

2026-01-16

AI Summary

This study addresses the challenge of detecting complex anomalies in smart grids—arising from cyberattacks, equipment failures, and natural disasters—where conventional machine learning methods underperform due to limited capacity in modeling high-dimensional nonlinear patterns and vulnerability to adversarial attacks. To overcome these limitations, the authors propose QUPID, a partitioned quantum neural network that leverages quantum-enhanced feature representations to improve anomaly detection performance. Furthermore, they introduce R-QUPID, an extension incorporating differential privacy to enhance robustness. This work represents the first integration of differential privacy into quantum neural networks for smart grid anomaly detection and employs a partitioned architecture to effectively mitigate scalability issues inherent in quantum models. Experimental results demonstrate that both QUPID and R-QUPID significantly outperform traditional approaches across multiple scenarios, achieving substantial gains in detection accuracy and adversarial robustness.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• anomaly detection
• smart grid
• quantum machine learning
• adversarial robustness
• scalability

Innovation

• Quantum Neural Network
• Anomaly Detection
• Smart Grid
• Partitioned Architecture
• Differential Privacy