Quantum key distribution (QKD) is an emerging field of cryptography which utilizes the unique properties exhibited by quantum mechanics to generate and distribute cryptographic keys between two authorized parties, Alice and Bob, in the presence of an eavesdropper, Eve. The communicating parties are connected by a quantum channel and an authenticated classical channel. However, owing to recent advances in quantum computing, classical cryptography is becoming increasingly insecure. Consequently, QKD has become more crucial as a means of secure communication. Among the tools being used to enhance the security of QKD systems are artificial intelligence and machine learning algorithms. These algorithms can detect and identify sources of interference, model and predict noise, and analyze the security of the system. Additionally, machine learning algorithms have also been used to optimize the protocol parameters of QKD systems as well as to detect and mitigate attacks against these systems. Thus, as QKD continues to develop, machine learning algorithms are expected to become increasingly prevalent, thereby enhancing the security of communication systems.
The primary problem concerns how best to utilize machine learning to enhance the security of quantum key cryptosystems. This problem is best addressed by leveraging artificial intelligence and machine learning algorithms to detect and prevent attacks on the system and enhance the robustness of the QKD system. Moreover, further investigation into how machine learning can be used to develop enhanced algorithms for analyzing the data generated by the system is essential. This could improve the efficiency and robustness of the QKD system while providing novel ways to analyze the data generated by the system. Thus, leveraging artificial intelligence and machine learning in QKD could enable the development of novel quantum key distributions, which may in turn result in the development of novel QKD protocols.
Topics of interest, include but are not limited to:
- Quantum secure direct communication networks
- Quantum key distribution
- Novel quantum communication protocols
- Free-space quantum communications
- Quantum machine learning
- Quantum metrology
- Quantum-classical hybrid computing
- Post-quantum cryptography
Keywords:
Security, Quantum Entanglement, Quantum Machine Learning, Quantum Cryptography, Quantum Key Distribution, Quantum Computing, Quantum Teleportation, Single-Photon Sources, Quantum and Free-Space Communications, Integrated Quantum Photonics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Quantum key distribution (QKD) is an emerging field of cryptography which utilizes the unique properties exhibited by quantum mechanics to generate and distribute cryptographic keys between two authorized parties, Alice and Bob, in the presence of an eavesdropper, Eve. The communicating parties are connected by a quantum channel and an authenticated classical channel. However, owing to recent advances in quantum computing, classical cryptography is becoming increasingly insecure. Consequently, QKD has become more crucial as a means of secure communication. Among the tools being used to enhance the security of QKD systems are artificial intelligence and machine learning algorithms. These algorithms can detect and identify sources of interference, model and predict noise, and analyze the security of the system. Additionally, machine learning algorithms have also been used to optimize the protocol parameters of QKD systems as well as to detect and mitigate attacks against these systems. Thus, as QKD continues to develop, machine learning algorithms are expected to become increasingly prevalent, thereby enhancing the security of communication systems.
The primary problem concerns how best to utilize machine learning to enhance the security of quantum key cryptosystems. This problem is best addressed by leveraging artificial intelligence and machine learning algorithms to detect and prevent attacks on the system and enhance the robustness of the QKD system. Moreover, further investigation into how machine learning can be used to develop enhanced algorithms for analyzing the data generated by the system is essential. This could improve the efficiency and robustness of the QKD system while providing novel ways to analyze the data generated by the system. Thus, leveraging artificial intelligence and machine learning in QKD could enable the development of novel quantum key distributions, which may in turn result in the development of novel QKD protocols.
Topics of interest, include but are not limited to:
- Quantum secure direct communication networks
- Quantum key distribution
- Novel quantum communication protocols
- Free-space quantum communications
- Quantum machine learning
- Quantum metrology
- Quantum-classical hybrid computing
- Post-quantum cryptography
Keywords:
Security, Quantum Entanglement, Quantum Machine Learning, Quantum Cryptography, Quantum Key Distribution, Quantum Computing, Quantum Teleportation, Single-Photon Sources, Quantum and Free-Space Communications, Integrated Quantum Photonics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.