  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.
