Network Intelligence with Quantum Computing in 6G and B6G: Design Principles and Future Directions

Network intelligence in 6G systems and beyond may require computing power and computation time hard to reach in current deployments. While the employment of quantum computers for supporting Quantum Machine Learning emerged as a viable solution to overcome this issue, their integration within a network architecture still represents an uncovered research topic. To bridge this gap, this paper illustrates the design principles of centralized and distributed architectures, where quantum computers are deployed in the remote cloud or geographically distributed at the edge, respectively. The advantages and disadvantages of the resulting network architectures are investigated to point out open issues and future research directions.