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  • Letter
  • Open Access

Artificial neural network syndrome decoding on IBM quantum processors

Brhyeton Hall1,*, Spiro Gicev1,†, and Muhammad Usman1,2,‡

  • 1School of Physics, The University of Melbourne, Parkville, 3010 Victoria, Australia
  • 2Data61, CSIRO, Research Way, Clayton, 3168 Victoria, Australia

  • *Contact author: brhyeton@unimelb.edu.au
  • †Contact author: gicevs@unimelb.edu.au
  • ‡Contact author: musman@unimelb.edu.au
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Phys. Rev. Research 6, L032004 – Published 8 July, 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.L032004

Abstract

Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and benchmarking of Artificial Neural Network (ANN) decoding on IBM quantum processors. We demonstrate that ANNs can efficiently decode syndrome measurement data from heavy-hexagonal code architecture and apply appropriate corrections to facilitate error protection. The current physical error rates of IBM devices are above the code's threshold and restrict the scope of our ANN decoder for logical error rate suppression. However, our work confirms the applicability of ANN decoding methods of syndrome data retrieved from experimental devices and establishes machine learning as a promising pathway for quantum error correction when quantum devices with below threshold error rates become available in the near future.

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Outline

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Phys. Rev. Research 6, L032004– Published 8 July, 2024
  • Received 29 November 2023
  • Accepted 21 June 2024
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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