Learning high-accuracy error decoding for quantum processors
Problem
Building large-scale quantum computers requires accurately decoding noisy syndrome information from quantum error-correction codes such as the surface code.
Why it matters
Effective error correction is a prerequisite for building useful large-scale quantum computers.
What the AI did
A recurrent, transformer-based neural network was trained to decode the surface code, including on approximate synthetic data, and evaluated on real quantum processor data.
Results
Outperforms other state-of-the-art decoders on real data from Google's Sycamore processor for distance-3 and distance-5 surface codes; maintains advantage up to distance 11 on simulated data with realistic noise.
Impact
Advances decoding accuracy for the leading quantum error-correction code on real hardware.
Record
| Venue | Nature |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Johannes Bausch, Andrew Senior, Francisco J. H. Heras, Thomas Edlich, Alex Davies, Michael E. Newman, Cody Jones, Kevin J. Satzinger et al. |
| Citations | 90 (OpenAlex, at import time) |
| References | 114 |
| Model or method | Recurrent, transformer-based neural network decoder |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41586-024-08148-8 |
| Open access | hybrid |
| Keywords | quantum error correction, surface code, neural network decoder, transformer, quantum computing |
Importance rating
significant — thousands to millions affected
Abstract states error correction is required for building large-scale quantum computers and calls surface-code decoding a key challenge; foundational to the quantum computing field, though no beneficiary scale is quantified.
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