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Linear-time general decoding algorithm for the surface code.

Andrew S Darmawan1, David Poulin2,3

  • 1Department of Applied Physics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

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Summary
This summary is machine-generated.

This study introduces an improved quantum error correction decoder for surface codes. The new method accounts for complex noise, outperforming standard algorithms for better quantum computing stability.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Error Correction

Background:

  • Quantum error correction is crucial for fault-tolerant quantum computing.
  • Conventional decoders often assume simplified noise models, limiting performance.
  • Physical noise in quantum systems can be complex, featuring coherences and correlations.

Purpose of the Study:

  • To develop an efficient decoder for surface codes that incorporates realistic noise features.
  • To demonstrate the performance improvement over existing decoding algorithms.

Main Methods:

  • Developed a novel decoder for the surface code architecture.
  • Incorporated general noise characteristics, including coherences and correlations.
  • Utilized tensor-network methods for approximate calculation of the logical channel.

Main Results:

  • The proposed decoder significantly outperforms the conventional matching algorithm.
  • Demonstrated superior performance across various noise models, including non-Pauli and spatially correlated noise.
  • The decoder effectively handles complex noise features.

Conclusions:

  • Accounting for physical noise features substantially improves quantum error correction protocols.
  • The developed decoder offers a more robust approach to quantum error correction for surface codes.
  • This work advances the development of fault-tolerant quantum computers.