Quantum Machine Learning for Cybersecurity: Applications and Challenges in the NISQ Era

Abstract

The rapid advancement of quantum computing presents both unprecedented opportunities and significant challenges for cybersecurity. Classical machine learning methods increasingly struggle with high-dimensional network traffic, evolving attack vectors, and real-time response requirements. Quantum machine learning (QML), which leverages quantum mechanical phenomena such as superposition and entanglement, has emerged as a promising paradigm for enhancing threat detection, anomaly analysis, and secure communication. This paper provides a comprehensive overview of QML applications in cybersecurity, examining key techniques including Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and hybrid quantum–classical approaches. The analysis evaluates recent implementations in intrusion detection, malware classification, and encrypted-traffic analytics, drawing on empirical evidence from benchmark datasets. Results indicate that QML models can achieve up to 94.6% accuracy in intrusion detection tasks, with quantum-enhanced frameworks demonstrating significant improvements in detection accuracy and threat response latency compared to conventional baselines. However, significant challenges persist, including hardware limitations of noisy intermediate-scale quantum (NISQ) devices, scalability constraints, and the vulnerability of quantum models to adversarial attacks. The paper concludes by outlining future research directions for noise-resilient QML architectures and the integration of quantum computing with agentic AI for proactive cybersecurity defense.