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.

翻译：大规模MIMO检测仍然面临挑战，因为精确或近似最大似然搜索难以扩展，而现有量子资源不足以通过QAOA直接求解全尺寸检测实例。为此，本文提出一种块状QAOA感知MIMO检测器（BQA-MD），其主要目标是通过重组检测链，使其能够与有限量子比特的局部量子子问题兼容。具体而言，BQA-MD结合了QR域中的块状QAOA感知预处理、符合标准的块状5G NR Gray-HUBO接口、MMSE导出的动态正则化块状目标函数以及K最优候选传播机制。在此框架下，固定尺寸的块构造使每个局部子问题具有统一的电路宽度和参数维度，从而为结构匹配的局部子问题实现参数迁移QAOA这一实用化策略。实验在16×16瑞利MIMO系统上采用16QAM调制，并通过经典模拟量子子程序进行。结果表明，正则化块状检测器性能优于未正则化版本，验证了所采用的块状目标函数及块状QAOA感知设计原理。实验还显示，参数迁移QAOA检测器的误码率性能接近正则化块状穷举参考基准，并明显优于直接训练QAOA，这支持了参数复用作为该框架内首选的QAOA实现策略。在测试场景中，MMSE在低信噪比区域仍具微弱优势，而参数迁移QAOA检测器从中等信噪比区域开始展现出高度竞争力。