Reviewing Quantum Artificial Intelligence in Techniques of Advanced Communications

Mhlambululi Mafu

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.

Neal Xiong, David Guo

Ram Krishan

It is anticipated that the capabilities of quantum computers will substantially advance the field of Artificial Intelligence (AI). Quantum computing is similar to conventional computing, which encodes data using bits of 0s and 1s. Quantum computing, however, has its variant of data encoding known as quantum or qubit. AI processes complex datasets and is useful in the development of algorithms to create better learning and understanding environments. Researchers are working on employing quantum computing techniques to achieve the goals of AI. This chapter reviews the correlation between quantum computers and artificial intelligence and its future scope.

Dr. Rakshitha Kiran P Dr. Samitha Khaiyum Dr. Pallavi Kulkarni Dr. Pavithra G. Shetty Prof. Raksha Kodnad R

Sudhir K. Routray, Sasmita Mohanty

Istiak Mahmud, Ahmed Abdelhadi

The integration of Artificial Intelligence (AI) into the field of quantum communication has emerged in recent years as a promising avenue for enhancing secure data transmission, error correction, and scalability in quantum networks. This paper offers a comprehensive survey of AI applications in quantum communication, with a focus on machine learning (ML) models such as neural networks and reinforcement learning, which are adapted to manage complex quantum challenges. Despite the growing body of research, current studies often concentrate on specific AI techniques or quantum applications, lacking a unified framework that evaluates their practical effectiveness, scalability, and cost-efficiency. Addressing this gap, our survey systematically reviews and categorizes AI-driven methodologies across core aspects like system security, transmission speed, and reliability. Through a critical comparison and ranking of existing techniques, this study identifies best practices and key advancements while highlighting areas requiring further exploration. By providing an in-depth analysis of AI's potential to transform quantum communication, this paper aims to serve as a foundational resource for researchers and practitioners seeking to develop resilient, adaptive, and economically viable quantum communication systems.