Efficient and scalable decoding of quantum codes is essential for high-performance quantum error correction. In this work, we introduce Reliable Subset Reduction (RSR), a reliability-driven preprocessing framework that leverages belief propagation (BP) statistics to identify and remove highly reliable qubits, substantially reducing the effective problem size. Additionally, we identify a degeneracy condition that allows high-order OSD to be simplified to order-0 OSD. By integrating these techniques, we present an ADOSD algorithm that significantly improves OSD efficiency. Our BP+RSR+ADOSD framework extends naturally to circuit-level noise and can handle large-scale codes with more than $10^4$ error variables. Through extensive simulations, we demonstrate improved performance over MWPM and Localized Statistics Decoding for a variety of CSS and non-CSS codes under the code-capacity noise model, and for rotated surface codes under realistic circuit-level noise. At low physical error rates, RSR reduces the effective problem size to less than 5\%, enabling higher-order OSD with accelerated runtime. These results highlight the practical efficiency and broad applicability of the BP+ADOSD framework for both theoretical and realistic quantum error correction scenarios.

翻译：高效且可扩展的量子纠错码译码是实现高性能量子纠错的关键。本文提出可靠子集约简（RSR），一种基于可靠性的预处理框架，该框架利用置信传播（BP）统计量识别并移除高可靠性的量子比特，从而显著降低有效问题规模。此外，我们发现一种退化条件，使得高阶有序统计译码（OSD）可简化为零阶OSD。通过整合这些技术，我们提出一种ADOSD算法，大幅提升了OSD的效率。我们的BP+RSR+ADOSD框架可自然地扩展到电路级噪声，并能处理包含超过$10^4$个错误变量的大规模纠错码。通过大量仿真实验，我们验证了在码容量噪声模型下，对于多种CSS与非CSS码，以及在电路级噪声下对于旋转表面码，该方法的性能均优于最小权重完美匹配（MWPM）与局部统计译码。在低物理错误率条件下，RSR能将有效问题规模缩减至5%以下，从而在加速运行时间的同时实现高阶OSD。这些结果凸显了BP+ADOSD框架在理论与实际量子纠错场景中的实用高效性与广泛适用性。