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Quantum Error Correction Fault Tolerance Quantum Machine Learning

Convolutional neural network based decoders for surface codes

arXiv
Authors: Simone Bordoni, Stefano Giagu

Year

2023

Paper ID

52798

Status

Preprint

Abstract Read

~2 min

Abstract Words

107

Citations

N/A

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2312.03508 [2] arXiv https://arxiv.org/abs/2312.03508 [3] Publisher https://arxiv.org/abs/2312.03508

Local Citation Graph (Related-Paper Links)

Current Paper #52798 #69034 Hardware-aware Low-latency Quan... #69036 CARVE-Q: Quantum-Proposed, Clas... #69025 Machine-Learning Optimization a... #69003 QBugLM: An Agentic Benchmarking...

External citation index: OpenAlex citation signal

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