Burhan Gulbahar2025-10-06202515361276, 155822481536-12761558-224810.1109/TWC.2024.3523135https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001061583&doi=10.1109%2FTWC.2024.3523135&partnerID=40&md5=f36e191ae49bdc813cb07004840be06bhttps://gcris.yasar.edu.tr/handle/123456789/8124Quantum approximate optimization algorithm (QAOA) with layer depth p is promising near-optimum performance and low complexity for NP-hard maximum-likelihood (ML) detection in n × n multi-input multi-output (MIMO) systems. Experimental challenges for ML detection on Noisy Intermediate-Scale Quantum (NISQ) computers arise from accumulated errors with large p and n. Recursive QAOA (RQAOA) is promising with small p by reducing complexity over n steps. In this article we modify RQAOA for p ≪ n with cost sorting and post-selection in m ≪ n steps 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 with n = 24 and 12 for BPSK and QPSK modulations respectively show near-optimum bit-error rate (BER) with p = 1 and m ≤ 4. Truncated version requires O(m n p) quantum and O(m n2) classical operations with low complexity. We experimentally implement QAOA combined with MV (QAOA-MV) for n ϵ [17 64] in IBM Eagle processor by observing superior performance of QAOA-MV over QAOA and reducing problem dimensions by at least n / 4. We generalize QAOA as cost-restricted uniform sampling (CRUS) oracle and approximately simulate for n ≤ 128 to obtain comparison benchmark for future QAOA experiments. © 2025 Elsevier B.V. All rights reserved.EnglishError Mitigation, Majority Voting, Massive Mimo, Ml Decoding, Recursive Quantum Approximate Optimization, Successive Interference Cancellation, Amplitude Shift Keying, Benchmarking, Delta Sigma Modulation, Error Statistics, Forward Error Correction, Maximum Likelihood Estimation, Np-hard, Quadrature Amplitude Modulation, Quadrature Phase Shift Keying, Time Difference Of Arrival, Approximate Optimization, Error Mitigation, Majority Voting, Massive Multi-input Multi-output, Maximum- Likelihood Detection, Maximum-likelihood Decoding, Multi-input Multi-output, Optimization Algorithms, Recursive Quantum Approximate Optimization, Successive Interference Cancellations, Quantum ComputersAmplitude shift keying, Benchmarking, Delta sigma modulation, Error statistics, Forward error correction, Maximum likelihood estimation, NP-hard, Quadrature amplitude modulation, Quadrature phase shift keying, Time difference of arrival, Approximate optimization, Error mitigation, Majority voting, Massive multi-input multi-output, Maximum- likelihood detection, Maximum-likelihood decoding, Multi-input multi-output, Optimization algorithms, Recursive quantum approximate optimization, Successive interference cancellations, Quantum computersArticle