Hybrid Quantum Machine Learning for Anomaly Detection in Critical Infrastructure Sensor Networks

This research explores the integration of quantum computing and machine learning for anomaly detection in critical infrastructure sensor networks. Anomaly detection is crucial for maintaining the reliability and security of these networks, where early detection of faults or intrusions can prevent catastrophic failures. Traditional machine learning methods often struggle with the high-dimensional and complex data generated by these systems. Hybrid quantum approaches, combining quantum algorithms with classical machine learning models, offer a promising solution by leveraging quantum computational advantages, such as quantum feature mapping and kernel methods. This paper investigates how these hybrid models can improve detection accuracy, reduce processing time, and handle large-scale data more efficiently. The potential of quantum machine learning for anomaly detection in critical infrastructure is discussed, along with the challenges posed by current quantum hardware limitations and future directions for research.