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Quantum-Enhanced Machine Learning for Next-Gen Cyber Defense
Abstract
As cyber threats become more complex, traditional machine learning increasingly struggles with real-time detection and response. Rising data volumes, adversarial tactics, and classical computing limits call for new cybersecurity approaches. This chapter explores quantum-enhanced machine learning (QeML), leveraging quantum parallelism, entanglement, and amplitude amplification to improve data processing, pattern recognition, and classification in complex threat landscapes. We present a QeML framework combining quantum kernel estimation and variational circuits within a supervised learning pipeline for intrusion detection. Experiments on benchmark datasets using quantum simulators and hardware show improved accuracy, resilience, and efficiency over classical models. Case studies highlight practical challenges and future directions for scalable deployment. This chapter provides a foundation for applying QeML in cyber defense, offering guidance for leveraging quantum advantage to protect digital infrastructure.
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