Learning high-accuracy error decoding for quantum processors
成果类型：
Article
署名作者：
Bausch, Johannes; Senior, Andrew W.; Heras, Francisco J. H.; Edlich, Thomas; Davies, Alex; Newman, Michael; Jones, Cody; Satzinger, Kevin; Niu, Murphy Yuezhen; Blackwell, Sam; Holland, George; Kafri, Dvir; Atalaya, Juan; Gidney, Craig; Hassabis, Demis; Boixo, Sergio; Neven, Hartmut; Kohli, Pushmeet
署名单位：
Alphabet Inc.; Google Incorporated; DeepMind
刊物名称：
Nature
ISSN/ISSBN：
0028-6421
DOI：
10.1038/s41586-024-08148-8
发表日期：
2024-11-28
关键词：
摘要：
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. A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network decoders for real quantum hardware.