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-s...
| Authors | Ge Yan, Shanchuan Li, Shiyi Xiao et al. |
|---|---|
| Primary category | quant-ph |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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