Learning high-accuracy error decoding for quantum processors

Johannes Bausch (), Andrew W. Senior (), Francisco J. H. Heras, Thomas Edlich, Alex Davies, Michael Newman, Cody Jones, Kevin Satzinger, Murphy Yuezhen Niu, Sam Blackwell, George Holland, Dvir Kafri, Juan Atalaya, Craig Gidney, Demis Hassabis, Sergio Boixo, Hartmut Neven and Pushmeet Kohli
Additional contact information
Johannes Bausch: Google DeepMind
Andrew W. Senior: Google DeepMind
Francisco J. H. Heras: Google DeepMind
Thomas Edlich: Google DeepMind
Alex Davies: Google DeepMind
Michael Newman: Google Quantum AI
Cody Jones: Google Quantum AI
Kevin Satzinger: Google Quantum AI
Murphy Yuezhen Niu: Google Quantum AI
Sam Blackwell: Google DeepMind
George Holland: Google DeepMind
Dvir Kafri: Google Quantum AI
Juan Atalaya: Google Quantum AI
Craig Gidney: Google Quantum AI
Demis Hassabis: Google DeepMind
Sergio Boixo: Google Quantum AI
Hartmut Neven: Google Quantum AI
Pushmeet Kohli: Google DeepMind

Nature, 2024, vol. 635, issue 8040, 834-840

Abstract: Abstract Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems1. Quantum error-correction codes2 present a way to reach this goal by encoding logical information redundantly into many physical qubits. A key challenge in implementing such codes is accurately decoding noisy syndrome information extracted from redundancy checks to obtain the correct encoded logical information. Here we develop a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code3. Our decoder outperforms other state-of-the-art decoders on real-world data from Google’s Sycamore quantum processor for distance-3 and distance-5 surface codes4. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk and leakage, utilizing soft readouts and leakage information. After training on approximate synthetic data, the decoder adapts to the more complex, but unknown, underlying error distribution by training on a limited budget of experimental samples. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

Date: 2024
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DOI: 10.1038/s41586-024-08148-8

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