Exploring Optimal and Suboptimal Beamforming Solutions Using Weighted Subspace-Search-VQE

This work explores the application of a Quantum algorithm, namely Weighted Subspace-Search-Variational-Quantum-Eigensolver (SSVQE), a variant of the traditional VQE algorithm, to predict optimal as well as sub-optimal sparse array configurations for the problem of beamforming. The optimization formulation is designed to maximize the Signal-to-Interference-plus-Noise Ratio (SINR), which is then reformulated into Quadratic Unconstrained Binary Optimization (QUBO) representation to serve as the Hamiltonian for the weighted SSVQE algorithm. The results obtained are presented along with classically obtained SINR plots to facilitate a comparison of the sparse array configurations. Our findings indicate that the output from the quantum algorithm precisely match with the results obtained from the classical method.