这篇论文提出了NTU框架，能让小代码训练的知识加速大解码器，在几类量子纠错码上都比现有方法好。
本文提出了神经转移统一（NTU）框架，用于高效训练基础解码器。NTU-Transformer解码器在平面表面码[361,1,19]上优于相关感知匹配，并扩展到[625,1,25]码。对于双变量自行车码[72,12,6]，在低物理错误率下超过Relay-BP。该方法通过代数结构对齐不同代码距离的解码任务，实现从小规模代码到大规模解码器的知识迁移。
Efficient foundation decoders for fault-tolerant quantum computing
Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code distances. However, their construction often faces a steep scaling barrier, as larger code distances rapidly amplify the cost of syndrome generation and neural optimization. To address this bottleneck, here we devise neural transfer unification (NTU), a unified framework for efficient foundation decoders. A central feature of NTU is its ability to align decoding tasks across code distances via algebraic structures shared by scalable code families, which enables knowledge learned on smaller codes to accelerate large-scale decoder training. We instantiate NTU as NTU-Transformer, a transformer-based neural decoder tailored for planar surface codes and bivariate bicycle codes. For planar surface codes under circuit-level noise, NTU-Transformer outperforms correlation-aware matching on the $[\![361,1,19]\!]$ code and further scales to the $[\![625,1,25]\!]$ code, where it exceeds standard matching through transfer adaptation. For the bivariate bicycle code with $[\![72,12,6]\!]$, it surpasses Relay-BP in the low-physical-error regime. These results establish our proposal as a scalable route to amortized cross-distance training of foundation decoders for fault-tolerant quantum processors.