Majority Voting With Recursive QAOA and Cost-Restricted Uniform Sampling for Maximum-Likelihood Detection in Massive MIMO
Burhan Gülbahar · IEEE Transactions on Wireless Communications · 2025
Quantum approximate optimization algorithm (QAOA) with layer depthpis promising near-optimum performance and low complexity for NP-hard maximum-likelihood (ML) detection inn×nmulti-input multi-output (MIMO) systems. Experimental challenges for ML detection on Noisy Intermediate-Scale Quantum (NISQ) computers arise from accumulated errors with largepandn. Recursive QAOA (RQAOA) is promising with smallpby reducing complexity overnsteps. In this article, we modify RQAOA forp≪nwith cost sorting and post-selection inm≪nsteps, and then integrate it with majority voting (MV) and successive interference cancellation (SIC) into the QAOA-MVSIC algorithm to tackle experimental challenges. We truncate QAOA circuits to further improve experimental feasibility. Simulations withn= 24 and 12 for BPSK and QPSK modulations, respectively, show near-optimum bit-error rate (BER) withp= 1 andm≤ 4. Truncated version requiresO(m n p) quantum andO(m n2) classical operations with low complexity. We experimentally implement QAOA combined with MV (QAOA-MV) fornϵ [17; 64] in IBM Eagle processor by observing superior performance of QAOA-MV over QAOA and reducing problem dimensions by at leastn/4. We generalize QAOA as cost-restricted uniform sampling (CRUS) oracle and approximately simulate forn≤ 128 to obtain comparison benchmark for future QAOA experiments.