Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

Bhoumik, Debasmita; Sen, Pinaki; Majumdar, Ritajit; Sur-Kolay, Susmita; J, Latesh Kumar K; Iyengar, S. S.

doi:10.48550/arxiv.2110.10896

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Another interesting novel idea comes from the application of machine learning techniques to an ensemble of classical decoders [32]. Other decoders based on machine learning have been recently tested by Bhoumik et al [33]. A state of the art decoder, scalable to high distance surface codes, has been created by Meinerz et al [34] combining convolutional neural networks (to preprocess local information) with a conventional algorithm.…”

Section: Related Workmentioning

confidence: 99%

Convolutional neural network based decoders for surface codes

Bordoni

Giagu

2023

Quantum Inf Process

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The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorithms based on artificial neural networks. This work reports a study of decoders based on convolutional neural networks, tested on different code distances and noise models. The results show that decoders based on convolutional neural networks have good performance and can adapt to different noise models. Moreover, explainable machine learning techniques have been applied to the neural network of the decoder to better understand the behaviour and errors of the algorithm, in order to produce a more robust and performing algorithm.

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Section: Related Workmentioning

confidence: 99%

Convolutional neural network based decoders for surface codes

Bordoni

Giagu

2023

Quantum Inf Process

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“…Other than this, look-up table [29] and machine learning (ML) based decoders have been proposed for surface codes as well [26,30,31]. Even though ML decoders have been observed to outperform MWPM decoders for the symmetric noise model, it is not known whether the same ML model with the same parameters will provide the best performance for an asymmetric model with any degree of asymmetry (since the structure becomes more and more rectangular).…”

Section: Surface Codesmentioning

confidence: 99%

Surface code design for asymmetric error channels

Azad

Lipińska

Shilpa

et al. 2022

IET Quantum Communication

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Surface codes are quantum error correcting codes typically defined on a 2D array of qubits. A [dx, dz] surface code design is being introduced, where dx(dz) represents the distance of the code for bit (phase) error correction, motivated by the fact that the severity of bit flip and phase flip errors in the physical quantum system is asymmetric. We present pseudo‐threshold and threshold values for the proposed surface code design for asymmetric error channels in the presence of various degrees of asymmetry of PauliX^,trueY^ $\text{Pauli}\,\hat{X},\,\hat{Y}$, andZ^ $\text{and}\,\hat{Z}$ errors in a depolarisation channel. We demonstrate that compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo‐threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. Our results show that for low degree of asymmetry, it is advantageous to increase dx along with dz. However, as the asymmetry of the channel increases, higher pseudo‐threshold is obtained with increasing dz when dx is kept constant at a low value. Additionally, we also show that the advantage in the pseudo‐threshold rates begins to saturate for any possible degree of asymmetry in the error channel as the surface code asymmetry is continued to be increased.

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“…Other than this, look-up table [27] and machine learning (ML) based decoders have been proposed for surface codes as well [28][29][30]. However, for this study, we stick with the MWPM decoder.…”

Section: Surface Codesmentioning

confidence: 99%

Surface Code Design for Asymmetric Error Channels

Azad,

Lipińska,

Mahato

et al. 2021

Preprint

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Surface codes are quantum error correcting codes normally defined on 2D arrays of qubits. In this paper, we introduce a surface code design based on the fact that the severity of bit flip and phase flip errors in the physical quantum systems is asymmetric. For our proposed surface code design for asymmetric error channels, we present pseudo-threshold and threshold values in the presence of various degrees of asymmetry of Pauli X, Ŷ , and Ẑ errors in a depolarization channel. We show that, compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo-threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. We also demonstrate that as the asymmetry of the surface code increases, the advantage in the pseudo-threshold rates begins to saturate for any degree of asymmetry in the channel.

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Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

Cited by 4 publications

References 12 publications

Convolutional neural network based decoders for surface codes

Convolutional neural network based decoders for surface codes

Surface code design for asymmetric error channels

Surface Code Design for Asymmetric Error Channels

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