Efficient foundation decoders for fault-tolerant quantum computing
For quantum computing researchers, this provides a scalable method to train high-capacity neural decoders for large code distances, reducing training cost.
The authors propose neural transfer unification (NTU), a framework for training foundation decoders for fault-tolerant quantum computing that transfers knowledge from small to large code distances. Their NTU-Transformer decoder outperforms correlation-aware matching on a [361,1,19] planar surface code and scales to a [625,1,25] code, and surpasses Relay-BP on a [72,12,6] bivariate bicycle code in the low-physical-error regime.
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.