Quantum-Assisted Combinatorial Optimization for Reconfigurable Intelligent Surfaces in Smart Electromagnetic Environments

We have recently seen a surge in interest in leveraging reconfigurable intelligent surfaces (RISs) in smart radio environments. One critical question is how to efficiently optimize the phase configuration that results in the desired reflective wavefront. In this article, we proposed a physics-based optimization approach inspired by the statistical mechanics of correlated spins and adiabatic quantum computing (QC). The new concept is based on the isomorphism of electromagnetic (EM) scattered power and the Ising Hamiltonian. As a result, the problem of optimizing phase configuration is transformed into the problem of finding the ground state of the target Ising Hamiltonian. We successfully demonstrate the feasibility of combinatorial optimization for weighted beamforming and diffusive scattering applications using this framework.