Quantum key distribution through quantum machine learning: a research review
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Quantum Science and Technology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frqst.2025.1575498/full |
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| author | Krupa Purohit Ajay Kumar Vyas |
| author_facet | Krupa Purohit Ajay Kumar Vyas |
| author_sort | Krupa Purohit |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b78b4abc3a774fee919d60616d98e46d |
| institution | OA Journals |
| issn | 2813-2181 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Quantum Science and Technology |
| spelling | doaj-art-b78b4abc3a774fee919d60616d98e46d2025-08-20T01:56:05ZengFrontiers Media S.A.Frontiers in Quantum Science and Technology2813-21812025-05-01410.3389/frqst.2025.15754981575498Quantum key distribution through quantum machine learning: a research reviewKrupa PurohitAjay Kumar VyasQuantum 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.https://www.frontiersin.org/articles/10.3389/frqst.2025.1575498/fullquantum cryptography (QC)quantum machine learning (QML)quantum key distribution (QKD)quantum convoluted neural networks (QCNN)quantum support vector machine (QSVM)eavesdropping detection |
| spellingShingle | Krupa Purohit Ajay Kumar Vyas Quantum key distribution through quantum machine learning: a research review Frontiers in Quantum Science and Technology quantum cryptography (QC) quantum machine learning (QML) quantum key distribution (QKD) quantum convoluted neural networks (QCNN) quantum support vector machine (QSVM) eavesdropping detection |
| title | Quantum key distribution through quantum machine learning: a research review |
| title_full | Quantum key distribution through quantum machine learning: a research review |
| title_fullStr | Quantum key distribution through quantum machine learning: a research review |
| title_full_unstemmed | Quantum key distribution through quantum machine learning: a research review |
| title_short | Quantum key distribution through quantum machine learning: a research review |
| title_sort | quantum key distribution through quantum machine learning a research review |
| topic | quantum cryptography (QC) quantum machine learning (QML) quantum key distribution (QKD) quantum convoluted neural networks (QCNN) quantum support vector machine (QSVM) eavesdropping detection |
| url | https://www.frontiersin.org/articles/10.3389/frqst.2025.1575498/full |
| work_keys_str_mv | AT krupapurohit quantumkeydistributionthroughquantummachinelearningaresearchreview AT ajaykumarvyas quantumkeydistributionthroughquantummachinelearningaresearchreview |