About this Research Topic
Machine Learning Applications in Quantum Communication Networks is an emerging field of cryptography that leverages the unique properties of quantum mechanics to generate and distribute cryptographic keys securely. Quantum key distribution (QKD) is at the forefront of this field, providing a means for two authorized parties, Alice and Bob, to communicate securely even in the presence of an eavesdropper, Eve. The increasing insecurity of classical cryptography due to advances in quantum computing has made QKD more crucial than ever. Recent studies have shown that artificial intelligence and machine learning algorithms can significantly enhance the security of QKD systems by detecting and identifying sources of interference, modeling and predicting noise, and analyzing system security. Despite these advancements, there remain significant gaps in understanding how best to utilize these technologies to optimize QKD protocols and mitigate attacks, necessitating further investigation.
This Research Topic aims to explore the integration of machine learning algorithms into quantum key distribution systems to enhance their security and robustness. By focusing on advanced algorithms capable of predicting and mitigating security threats, and by enhancing protocol robustness, the research seeks to revolutionize the robustness and operational efficacy of QKD systems. A deeper examination into AI's role in processing and analyzing quantum data could potentially lead to groundbreaking improvements in quantum key distribution technologies.
To gather further insights into the boundaries of machine learning applications in quantum communication networks, we welcome articles addressing, but not limited to, the following themes:
• Quantum secure direct communication networks;
• Quantum key distribution protocols and enhancements;
• Novel quantum communication protocols;
• Free-space quantum communications;
• Applications of quantum machine learning;
• Innovations in quantum metrology;
• Hybrid quantum-classical computing systems;
• Advanced studies in post-quantum cryptography.
This Research Topic aims to explore the integration of machine learning algorithms into quantum key distribution systems to enhance their security and robustness. By focusing on advanced algorithms capable of predicting and mitigating security threats, and by enhancing protocol robustness, the research seeks to revolutionize the robustness and operational efficacy of QKD systems. A deeper examination into AI's role in processing and analyzing quantum data could potentially lead to groundbreaking improvements in quantum key distribution technologies.
To gather further insights into the boundaries of machine learning applications in quantum communication networks, we welcome articles addressing, but not limited to, the following themes:
• Quantum secure direct communication networks;
• Quantum key distribution protocols and enhancements;
• Novel quantum communication protocols;
• Free-space quantum communications;
• Applications of quantum machine learning;
• Innovations in quantum metrology;
• Hybrid quantum-classical computing systems;
• Advanced studies in 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.
