  Reconfigurable antenna multiple-input multiple-output (MIMO) is a
foundational technology for the continuing evolution of cellular systems,
including upcoming 6G communication systems. In this paper, we address the
problem of flexible/reconfigurable antenna configuration selection for
point-to-point MIMO antenna systems by using physics-inspired heuristics.
Firstly, we optimize the antenna configuration to maximize the signal-to-noise
ratio (SNR) at the receiver by leveraging two basic heuristic solvers, i.e.,
coherent Ising machines (CIMs), that mimic quantum mechanical dynamics, and
quantum annealing (QA), where a real-world QA architecture is considered
(D-Wave). A mathematical framework that converts the configuration selection
problem into CIM- and QA- compatible unconstrained quadratic formulations is
investigated. Numerical and experimental results show that the proposed designs
outperform classical counterparts and achieve near-optimal performance (similar
to exhaustive search with exponential complexity) while ensuring polynomial
complexity. Moreover, we study the optimal antenna configuration that maximizes
the end-to-end Shannon capacity. A simulated annealing (SA) heuristic which
achieves near-optimal performance through appropriate parameterization is
adopted. A modified version of the basic SA that exploits parallel tempering to
avoid local maxima is also studied, which provides additional performance
gains. Extended numerical studies show that the SA solutions outperform
conventional heuristics (which are also developed for comparison purposes),
while the employment of the SNR-based solutions is highly sub-optimal.
