Integrating Quantum Communication with Machine Learning: a review

Abstract

Quantum communication and machine learning convergence is one of the promising directions in the development of communication systems that could be secure, efficient, and scalable. The modern communication networks employ quantum communication technologies, such as quantum key distribution (QKD), entangement, and teleportation, that can ensure high security and allow the transmission of reliable data. Combined with machine learning, these technologies can be used to process data better, provide better security, and perform better in many applications, including the Internet of Things (IoT), intelligent communication networks, health care, and artificial intelligence. This paper will provide a review of the key principles of quantum communication and the application of machine learning in communication systems, and how the two concepts can be applied to advantage. Besides that, the paper discusses critical technical problems, such as scalability, system integration, and the unavailability of standardization, that constrain the present application of hybrid quantum-classical systems. In addition, the future research directions are mentioned, specifically quantum-enhanced federated learning and the construction of a quantum internet. In general, this paper offers an analytical and systematic review of the field, highlighting the existing challenges, as well as the opportunities that may be explored in the future to establish next-generation intelligent communication systems.