Quantum key distribution through quantum machine learning: a research review

Abstract

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Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols plays a crucial role in enhancing security measures, addressing previously inaccessible threats, and improving cryptographic efficiency. Key research areas in quantum cryptography include Quantum Key Distribution (QKD), eavesdropping detection, QSDC, security analysis of QKD protocols, post-quantum cryptography, Quantum Network Security & Intrusion Detection, Quantum-secure communication beyond QKD, quantum random number generation, Quantum Secure Multi-Party Computation (QSMPC), Quantum Homomorphic Encryption (QHE), and privacy-preserving computation. QML algorithms improve the key generation of QKD, by improving quantum state selection and reducing measurements. This also allows them to increase efficiency because it identifies trends in errors and applies corrections, making quantum cryptography a more dependable option. With intelligent processing machine learning is excellent at handling complex, high-dimensional data-this may provide a viable strategy for enhancing QKD performance and increasingly real-world secure quantum communication networks. This review will explore current research gaps and future developments in QKD, security analysis of QKD protocols, and eavesdropping detection by leveraging various QML algorithms.

Keywords

• quantum cryptography (QC)
• quantum machine learning (QML)
• quantum key distribution (QKD)
• quantum convoluted neural networks (QCNN)
• quantum support vector machine (QSVM)
• eavesdropping detection