In this paper, we discuss how certain radio access network optimization
problems can be modelled using the concept of constraint satisfaction problems
in artificial intelligence, and solved at scale using a quantum computer. As a
case study, we discuss root sequence index (RSI) assignment problem - an
important LTE/NR physical random access channel configuration related
automation use-case. We formulate RSI assignment as quadratic unconstrained
binary optimization (QUBO) problem constructed using data ingested from a
commercial mobile network, and solve it using a cloud-based commercially
available quantum computing platform. Results show that quantum annealing
solver can successfully assign conflict-free RSIs. Comparison with well-known
heuristics reveals that some classic algorithms are even more effective in
terms of solution quality and computation time. The non-quantum advantage is
due to the fact that current implementation is a semi-quantum proof-of-concept
algorithm. Also, the results depend on the type of quantum computer used.
Nevertheless, the proposed framework is highly flexible and holds tremendous
potential for harnessing the power of quantum computing in mobile network
automation.

本文探讨了如何利用人工智能的约束满足问题来建模和解决一些特定的无线电接入网络优化问题，并用量子计算机来解决。以 LTE/NR 的 RSI 分配问题为例，通过商业移动网络中摄入的数据来构建一个二次无约束二进制优化问题，并使用云平台提供的量子计算机来解决它。结果表明，量子退火求解器可以成功地分配无冲突的 RSI。与著名的启发式算法相比，一些经典算法在解决方案质量和计算时间方面更加有效。该框架非常灵活，并有潜力在移动网络自动化中利用量子计算的威力。