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检测仍然具有挑战性，因为精确或近最大似然搜索难以扩展，同时现有量子资源不足以通过QAOA直接求解全尺寸检测实例。因此，本文提出一种基于Block-QAOA感知的MIMO检测器（BQA-MD），其主要目的是重构检测链，使其兼容有限量子比特的局部量子子问题。具体而言，BQA-MD结合了QR域内的块QAOA感知预处理、与标准一致的5G NR Gray-HUBO分块接口、基于MMSE的动态正则化分块目标函数以及K-best候选传播。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，从而支持参数迁移QAOA作为结构匹配局部子问题的实际实现策略。实验在采用16QAM调制的16x16瑞利MIMO系统上，通过量子子例程的经典模拟进行。结果表明，正则化分块检测器相较于非正则化版本性能提升，验证了所采用的分块目标函数和块QAOA感知设计原理。结果还表明，参数迁移QAOA检测器几乎匹配正则化分块穷举参考，并在误码率上明显优于直接训练QAOA，从而支持将参数重用作为所提框架内首选的QAOA实现策略。在测试设置中，MMSE在低信噪比区域仍略优，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。