Hybrid Quantum–Classical Machine Learning for Cyber Threat Detection: A Comparative Assessment of Quantum Kernel and Variational Quantum Circuit Techniques

Abstract

The growing complexity of cyber threats has revealed the limitations of classical machine learning techniques, motivating the exploration of Quantum Machine Learning in threat detection systems for enhancing detection accuracy and computational efficiency. This study presents a comparative evaluation of two prominent quantum machine learning approaches: quantum kernel methods and variational quantum circuits, applied across diverse threat detection tasks. Our analysis, supported by recent empirical findings, demonstrates that hybrid quantum-classical architectures can reach or surpass classical baselines while requiring few qubits, shallow circuits, and reduced parameter count. Despite these promising results, several challenges remain, including data loading and state preparation overheads, trainability constraints, and limited empirical validation. In response, we propose theoretical guidance and practical criteria to facilitate reproducible and effective application of Quantum Machine Learning in cyber threat detection, bridging the gap between emerging theory and empirical practice.