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Neural network decoder for near-term surface-code experiments

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Varbanov, Boris M., Serra-Peralta, Marc, Byfield, David, Terhal, Barbara M.·2025·Physical Review Research
DOI 10.1103/physrevresearch.7.013029

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Abstract

Neural network decoders can achieve a lower logical error rate compared to conventional decoders, like minimum-weight perfect matching, when decoding the surface code. Furthermore, these decoders require no prior information about the physical error rates, making them highly adaptable. In this study, we investigate the performance of such a decoder using both simulated and experimental data obtained from a transmon-qubit processor, focusing on small-distance surface codes. We first show that the neural network typically outperforms the matching decoder due to better handling of errors leading to multiple correlated syndrome defects, such as Y errors. When applied to the experimental data of Google Quantum AI [R. Acharya , ], the neural network decoder achieves logical error rates approximately 25 % lower than minimum-weight perfect matching, approaching the performance of a maximum-likelihood decoder. To demonstrate the flexibility of this decoder, we incorporate the soft information available in the analog readout of transmon qubits and evaluate the performance of this decoder in simulation using a symmetric Gaussian-noise model. Considering the soft information leads to an approximately 10 % lower logical error rate, depending on the probability of a measurement error. The good logical performance, flexibility, and computational efficiency make neural network decoders well-suited for near-term demonstrations of quantum memories.

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  • Neural networks have been a popular choice for ML decoders, and they can handle highly correlated noise, even beyond circuit-level noise descriptions.

    “To implement ML decoders, neural networks have been a popular choice, and they can also handle highly correlated noise, even beyond circuit-level noise descriptions [204, 205].”

    From Quantum error correction and fault tolerance: A comprehensive tutorial (2026) · cites this paper as [205]

    The Introduction describes neural-network decoders as an established, adaptable approach, and the circuit-level simulation results show this decoder learning correlations between defects caused by Y errors. The Discussion reports good performance on experimental data containing error sources omitted from the training model, but says learning those omitted errors through experimental training is an expectation, not a demonstrated ability to handle highly correlated noise beyond circuit-level descriptions. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Varbanov, B. M., Serra-Peralta, M., Byfield, D., & Terhal, B. M.
Affiliations
  • Delft University of Technology (NL)
  • Riverlane (United Kingdom) (GB)
Corresponding author
Varbanov, Boris M.
ORCID
Venue
Physical Review Research
Publisher
American Physical Society (APS)
Published
2025-01-08
Volume
7
Issue
1
ISSN
2643-1564 (ISSN-L)
DOI
10.1103/physrevresearch.7.013029
OpenAlex
W4406171114
Cited by
20
References
11
Citations by year
2026: 10 · 2025: 8 · 2024: 2
FWCI
7.28
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review Research (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (601.QT.001-1, 101113946)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
Keywords
neural network decoders, surface code, quantum error correction, transmon qubits, logical error rate, minimum-weight perfect matching, measurement error, quantum memory

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