Quantum information network research aims to achieve the interconnection of large-scale quantum nodes with the ability to generate, store, transmit, and manipulate quantum states, thereby building a fundamental platform to support the widespread application of quantum information technology. On the one hand, this research direction focuses on the architecture design of the global quantum Internet, striving to provide a theoretical basis and technical guidance for the construction of large-scale and wide-area quantum information networks in the future. On the other hand, this research disrection aims to introduce some solutions, such as efficient routing algorithms and fair resource allocation schemes, to overcome the limitations of inherent characteristics (e.g., quantum decoherence and probabilistic operations) in quantum information networks, thus enhanceing the perfromance of entanglement distribution between distant quantum nodes.

Quantum Artificial Intelligence (QAI) is an emerging research field that integrates quantum computing and artificial intelligence. It aims to use the powerful computing power and quantum properties of quantum computing (such as superposition, entanglement, and quantum tunneling) to promote the development of intelligent algorithms and the efficient solution of complex problems. Research in this field focuses on solving challenges such as large-scale optimization problems, machine learning model training, and data analysis that cannot be efficiently handled by traditional computing, while exploring the possibility of quantum technology enabling intelligent decision-making and reasoning. The research content of quantum artificial intelligence covers quantum machine learning, quantum optimization algorithms, quantum reinforcement learning, etc., and is expected to achieve technological innovation in scientific research and industrial applications in the future.