Hybrid Quantum-Classical Optimization for Joint Beamforming and Discrete Phase Shift Design in STAR-RIS 6G Networks

Simultaneous transmitting and reflecting reconfigurable-intelligent surface (STAR-RIS) has received significant attention as a potential technology for the sixth-generation (6G) of wireless networks due to its ability to boost signal coverage and enhance system efficiency. In this article, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimize beamforming and the discrete phase shifts of the STAR-RIS-assisted wireless network. In particular, we formulate a discrete optimization problem to maximize the total power transmitted to the ground users. This is achieved by optimizing the beamforming at the base station (BS) and the phase shift of the STAR-RIS under minimal power allocation for each user and the maximum power budget at the BS. Since the addressed problem is NP-hard, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamforming components and discrete phase shifts to search for near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks in terms of runtime and system power, and highlight its potential for practical deployment when solving medium- to large-scale networks.