Research progress of artificial intelligence empowered quantum communication and quantum sensing systems

TL;DR

This paper reviews how artificial intelligence enhances quantum communication and sensing systems by improving performance, security, and measurement precision, while also discussing current challenges and future prospects.

Contribution

It provides a comprehensive overview of AI applications in quantum systems, highlighting recent advancements and identifying key challenges for practical implementation.

Findings

AI improves quantum key distribution performance and security.

AI enhances high-precision quantum measurements.

Challenges include efficient algorithm deployment and system control.

Abstract

Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.