Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.

翻译：近年来，大规模多输入多输出（MIMO）技术因能提升数据速率、改善信号质量并在复杂环境中提供更优覆盖而得到广泛应用。本文研究了MIMO波束选择（MBS）问题，该问题已被证明是NP-hard且计算上难以处理的。为此，我们采用能够为大规模组合优化问题提供更快更高效解决方案的量子计算方法。我们将MBS表述为二次无界二进制优化形式，并利用相干伊辛机（CIM）物理机进行求解。我们将本方案与两种经典启发式算法——模拟退火和禁忌搜索——进行了性能对比。结果表明，本方案的平均性能分别提升261.23倍和20.6倍，证明基于CIM的解决方案在最优波束子集选择方面表现显著更优。该工作为实际5G运营展现了巨大潜力，并推动了量子计算在通信领域解决计算难题的应用。