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 # Fast AI-Based Pre-Decoders for Surface Codes

  ![](/sites/default/files/styles/wide/public/publications/Fast%20AI-based%20pre-decoders%20for%20surface%20codes.jpg?itok=3KryFXm4)

 Fast, scalable decoding architectures that operate in a block-wise parallel fashion across space and time are essential for real-time fault-tolerant quantum computing. We introduce a scalable AI-based pre-decoder for the surface code that performs local, parallel error correction at low latency, removing the majority of physical errors before passing residual syndromes to a downstream global decoder. This modular architecture is backend-agnostic and composes with arbitrary global decoding algorithms designed for surface codes, and our implementation is completely open source. Integrated with uncorrelated PyMatching, the pipeline achieves end-to-end decoding latencies of order $\\mathcal{O}(1 \\mu\\text{s})$ at large code distances on NVIDIA GB300 GPUs while reducing logical error rates (LERs) relative to global decoding alone. We observe further LER improvements by training a larger model, outperforming correlated PyMatching up to distance-13. We additionally introduce a noise-learning architecture that infers decoding weights directly from experimentally accessible syndrome statistics without requiring an explicit circuit-level noise model. We show that purely data-driven graph weight estimation can nearly match uncorrelated PyMatching and exceed correlated PyMatching in certain regimes, enabling highly-optimized decoding when hardware noise models are unknown or time-varying as well as training pre-decoders with realistic noise models. Together, these results establish a practical, modular, and high-throughput decoding framework suitable for large-distance surface-code implementations.



 ## Authors



Christopher Chamberland (NVIDIA)

Jan Olle (NVIDIA)

Muyuan Li (NVIDIA)

Scott Thornton (NVIDIA)

Igor Baratta (NVIDIA)

 

 

 ## Publication Date



Tuesday, April 14, 2026

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence)

[Computer Architecture](/index.php/research-area/computer-architecture)

 

 

 ## Uploaded Files



[Fast and Accurate AI-Based Pre-decoders for Surface Codes.pdf](https://d1qx31qr3h6wln.cloudfront.net/publications/Fast%20and%20Accurate%20AI-Based%20Pre-decoders%20for%20Surface%20Codes.pdf?VersionId=mCS9S3Pk68X5y0OKHSMXQTFS_3axnhY7 "Open file in new window")1.84 MB