Advances in artificial intelligence and machine learning for quantum communication applications

Abstract

Artificial intelligence (AI) and classical machine learning (ML) techniques have revolutionised numerous fields, including quantum communication. Quantum communication technologies rely heavily on quantum resources, which can be challenging to produce, control, and maintain effectively to ensure optimum performance. ML has recently been applied to quantum communication and networks to mitigate noise-induced errors and analyse quantum protocols. The authors systematically review state-of-the-art ML applications to advance theoretical and experimental central quantum communication protocols, specifically quantum key distribution, quantum teleportation, quantum secret sharing, and quantum networks. Specifically, the authors survey the progress on how ML and, more broadly, AI techniques have been applied to optimise various components of a quantum communication system. This has resulted in ultra-secure quantum communication protocols with optimised key generation rates as well as efficient and robust quantum networks. Integrating AI and ML techniques opens intriguing prospects for securing and facilitating efficient and reliable large-scale communication between multiple parties. Most significantly, large-scale communication networks have the potential to gradually develop the maturity of a future quantum internet.