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Recurrent neural network decoding of rotated surface codes based on distributed strategy |
| Fan Li(李帆)1, Ao-Qing Li(李熬庆)1, Qi-Di Gan(甘启迪)2, and Hong-Yang Ma(马鸿洋)2,† |
1 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China;
2 School of Sciences, Qingdao University of Technology, Qingdao 266033, China |
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Abstract Quantum error correction is a crucial technology for realizing quantum computers. These computers achieve fault-tolerant quantum computing by detecting and correcting errors using decoding algorithms. Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models. In this paper, we use a distributed decoding strategy, which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases. Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder. The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy. Then we test the decoding performance of our distributed strategy decoder, recurrent neural network decoder, and the classic minimum weight perfect matching (MWPM) decoder for rotated surface codes with different code distances under the circuit noise model, the thresholds of these three decoders are about 0.0052, 0.0051, and 0.0049, respectively. Our results demonstrate that the distributed strategy decoder outperforms the other two decoders, achieving approximately a 5 % improvement in decoding efficiency compared to the MWPM decoder and approximately a 2 % improvement compared to the recurrent neural network decoder.
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Received: 06 December 2023
Revised: 05 February 2024
Accepted manuscript online: 22 February 2024
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PACS:
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03.67.Pp
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(Quantum error correction and other methods for protection against decoherence)
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03.67.-a
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(Quantum information)
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87.64.Aa
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(Computer simulation)
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| Fund: Project supported by Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2021MF049, ZR2022LLZ012, and ZR2021LLZ001). |
Corresponding Authors:
Hong-Yang Ma
E-mail: hongyang_ma@aliyun.com
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Cite this article:
Fan Li(李帆), Ao-Qing Li(李熬庆), Qi-Di Gan(甘启迪), and Hong-Yang Ma(马鸿洋) Recurrent neural network decoding of rotated surface codes based on distributed strategy 2024 Chin. Phys. B 33 040307
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[1] Steane A 2022 arXiv:2204.11404[quant-ph]
[45] Krastanov S and Jiang L 2017 Scientific Reports 7 11003
[46] Overwater R W J, Babaie M and Sebastiano F 2022 IEEE Transactions on Quantum Engineering 3 1
[47] Duclos-Cianci G and Poulin D 2013 arXiv:1304.6100[quant-ph]
[48] Duivenvoorden K, Breuckmann N P, and Terhal B M 2018 IEEE Transactions on Information Theory 65 2545
[49] Hastings M B 2007 Phys. Rev. B 76 201102
[50] Wootton J 2015 Entropy 17 1946
[51] Graves A 2012 Supervised Sequence Labelling with Recurrent Neural Networks 385 37 |
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