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，而可用的量子资源不足以通过QAOA直接解决全尺寸检测实例。因此，本文提出了一种面向Block-QAOA感知的MIMO检测器（BQA-MD），其主要目标是重新组织检测链，使其与有限量子位的局部量子子问题兼容。具体而言，BQA-MD结合了QR域中的Block-QAOA感知预处理、符合标准的逐块5G NR Gray-HUBO接口、基于MMSE的动态正则化逐块目标函数以及K-best候选传播。在此框架内，固定大小的块构建使每个局部子问题具有统一的电路宽度和参数维度，进而使参数迁移QAOA成为结构匹配的局部子问题的实用实现策略。实验在16x16 Rayleigh MIMO系统（16QAM调制）上通过经典模拟量子子程序进行。结果表明，正则化逐块检测器相比其非正则化版本有所改进，验证了所采用的逐块目标函数和Block-QAOA感知设计原理。此外，实验结果还显示，参数迁移QAOA检测器几乎与正则化逐块穷举参考性能相当，且在误码率方面明显优于直接训练QAOA，从而支持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占优势，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。