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

TL;DR

This paper introduces a Block-QAOA-Aware MIMO detection framework that reorganizes the detection process for compatibility with limited quantum resources, utilizing parameter transfer QAOA to achieve near-optimal performance in large-scale MIMO systems.

Contribution

It proposes a novel blockwise detection method combining QAOA with parameter transfer, enabling scalable quantum-inspired detection for large MIMO systems.

Findings

Regularized blockwise detector outperforms unregularized version.

Parameter-transfer QAOA nearly matches exhaustive reference performance.

QAOA-based detector outperforms direct-training QAOA in BER from medium SNR onward.

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.