Hybrid Quantum-Classical Phase optimization in Reflecting PMS beamforming

Integrated Sensing and Communication (ISAC) is a linchpin in 6G communications. ISAC is enabled by identifying the targets followed by tracking the targets via communication. In supporting the tracking of targets, the Programmable Meta surfaces (PMS) beamforming reflection phases are optimized towards the target. Programmable capacitive PMS put forward uncomplicated antenna design with less energy utilization. In this paper, optimized beamforming in tracking targets is proposed by a Hybrid Quantum-Classical approach that utilizes the benefits of quantum algorithm that reduces computational time in generation of PMS phase dataset. Classical Optimization method stemming from Fourier transform approach in beamforming is augmented with Quantum Approximate Optimization Algorithm (QAOA) implemented to influence the optimal phase reflection with reduced computational time than classical optimization. The proposed method is validated with optimized beamforming towards multiple target scenario and shows the hybrid-quantum-classical model achieves 63.78% reduction in number of iterations with similar accuracy than traditional optimization in less computational time.