Block-QAOA-Aware Detection with Parameter Transfer for Large-Scale MIMO

2026-03-14

AI Summary

This work addresses the high computational complexity of maximum-likelihood detection in massive MIMO systems and the limited quantum resources available for solving the full problem via the Quantum Approximate Optimization Algorithm (QAOA). The authors propose BQA-MD, a block-wise QAOA-based detector that leverages QR decomposition for preprocessing, partitions the problem into fixed-size subproblems, and incorporates 5G NR-compatible Gray-HUBO modeling, MMSE-guided dynamic regularization, and a K-best candidate propagation mechanism to operate within constrained qubit budgets. For the first time, parameter-transfer QAOA is applied in this context, exploiting structural consistency across subproblems to enable parameter reuse. Evaluated on a 16×16 Rayleigh fading channel with 16-QAM, the proposed method achieves bit-error-rate performance approaching that of exhaustive search at medium-to-high SNRs and significantly outperforms directly trained QAOA, demonstrating the efficacy of the block-wise design and parameter transfer strategy.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Large-scale MIMO detection remains challenging because exact or near-maximum-likelihood search is difficult to scale, while available quantum resources are insufficient for directly solving full-size detection instances by QAOA. This paper therefore proposes a Block-QAOA-Aware MIMO Detector (BQA-MD), whose primary purpose is to reorganize the detection chain so that it becomes compatible with limited-qubit local quantum subproblems. Specifically, BQA-MD combines block-QAOA-aware preprocessing in the QR domain, a standards-consistent blockwise 5G NR Gray-HUBO interface, an MMSE-induced dynamic regularized blockwise objective, and K-best candidate propagation. Within this framework, fixed-size block construction gives every local subproblem a uniform circuit width and parameter dimension, which in turn enables parameter-transfer QAOA as a practical realization strategy for structurally matched local subproblems. Experiments are conducted on a 16x16 Rayleigh MIMO system with 16QAM using classical simulation of the quantum subroutine. The results show that the regularized blockwise detector improves upon its unregularized counterpart, validating the adopted blockwise objective and the block-QAOA-aware design rationale. They also show that the parameter-transfer QAOA detector nearly matches the regularized blockwise exhaustive reference and clearly outperforms direct-training QAOA in BER, thereby supporting parameter reuse as the preferred QAOA realization strategy within the proposed framework. In the tested setting, MMSE remains slightly better in the low-SNR region, whereas the parameter-transfer QAOA detector becomes highly competitive from the medium-SNR regime onward.

Problem

• Large-scale MIMO detection
• QAOA
• quantum resource limitation
• maximum-likelihood search
• parameter transfer

Innovation

• Block-QAOA
• parameter transfer
• MIMO detection
• quantum-inspired optimization
• regularized blockwise objective