https://doi.org/10.1140/epjqt/s40507-026-00492-0
Research
Decoding the surface code with a spatio-temporal transformer
Phillips Exeter Academy, 20 Main St., 03833, Exeter, NH, USA
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
12
November
2025
Accepted:
4
March
2026
Published online:
23
March
2026
Abstract
Quantum error correction is a major area of research for building fault-tolerant quantum computers. Recently, machine learning has emerged as a compelling approach to quantum error decoding because of its flexibility and high performance compared to classical methods. In this work, we introduce a spatio-temporal transformer with graph Laplacian positional encodings and factorized latent attention to efficiently deal with the topological structure of the code and the temporal correlations across measurement rounds. We perform experiments with simulated data of the surface code for small distances and demonstrate the potential for our model.
Key words: Quantum error correction / Surface code / Geometric deep learning / Neural decoders
© The Author(s) 2026
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
