A scalable and real-time neural decoder for topological quantum codes
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arXiv
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2025
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| author | Senior, Andrew W. Edlich, Thomas Heras, Francisco J. H. Zhang, Lei M. Higgott, Oscar Spencer, James S. Applebaum, Taylor Blackwell, Sam Ledford, Justin Žemgulytė, Akvilė Žídek, Augustin Shutty, Noah Cowie, Andrew Li, Yin Holland, George Brooks, Peter Beattie, Charlie Newman, Michael Davies, Alex Jones, Cody Boixo, Sergio Neven, Hartmut Kohli, Pushmeet Bausch, Johannes |
| author_facet | Senior, Andrew W. Edlich, Thomas Heras, Francisco J. H. Zhang, Lei M. Higgott, Oscar Spencer, James S. Applebaum, Taylor Blackwell, Sam Ledford, Justin Žemgulytė, Akvilė Žídek, Augustin Shutty, Noah Cowie, Andrew Li, Yin Holland, George Brooks, Peter Beattie, Charlie Newman, Michael Davies, Alex Jones, Cody Boixo, Sergio Neven, Hartmut Kohli, Pushmeet Bausch, Johannes |
| contents | Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of requirements remains unmet by a machine-learning decoder, nor by any decoder for promising resource-efficient codes such as the color code. Here we introduce AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and color codes at scale under realistic noise. For the color code, it is orders of magnitude faster than other high-accuracy decoders. We demonstrate real-time decoding faster than 1μs per cycle on commercial accelerators: for the surface code to distance 11, with better accuracy than leading real-time decoders; and the first real-time decoding of the color code to distance 9. These results support the practical application of a wider class of promising QEC codes, and establish a credible path towards high-accuracy, real-time neural decoding at the scales required for fault-tolerant quantum computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A scalable and real-time neural decoder for topological quantum codes Senior, Andrew W. Edlich, Thomas Heras, Francisco J. H. Zhang, Lei M. Higgott, Oscar Spencer, James S. Applebaum, Taylor Blackwell, Sam Ledford, Justin Žemgulytė, Akvilė Žídek, Augustin Shutty, Noah Cowie, Andrew Li, Yin Holland, George Brooks, Peter Beattie, Charlie Newman, Michael Davies, Alex Jones, Cody Boixo, Sergio Neven, Hartmut Kohli, Pushmeet Bausch, Johannes Quantum Physics Machine Learning 81P73, 68T07 I.2.0; J.2 Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of requirements remains unmet by a machine-learning decoder, nor by any decoder for promising resource-efficient codes such as the color code. Here we introduce AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and color codes at scale under realistic noise. For the color code, it is orders of magnitude faster than other high-accuracy decoders. We demonstrate real-time decoding faster than 1μs per cycle on commercial accelerators: for the surface code to distance 11, with better accuracy than leading real-time decoders; and the first real-time decoding of the color code to distance 9. These results support the practical application of a wider class of promising QEC codes, and establish a credible path towards high-accuracy, real-time neural decoding at the scales required for fault-tolerant quantum computation. |
| title | A scalable and real-time neural decoder for topological quantum codes |
| topic | Quantum Physics Machine Learning 81P73, 68T07 I.2.0; J.2 |
| url | https://arxiv.org/abs/2512.07737 |