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  • Open Access

Enhancing decoding performance using efficient error learning

Pavithran Iyer*,†

Aditya Jain

Stephen D. Bartlett

Joseph Emerson

  • Centre for Engineered Quantum Systems, School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia

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Phys. Rev. Applied 25, 054065 – Published 27 May, 2026

DOI: https://doi.org/10.1103/vtj7-nh6l

Abstract

Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on cycle error reconstruction (CER), which yields Pauli error rates on the n qubits of an error-correcting code. Although the total number of Pauli error rates needed to describe a general noise process is exponentially large in n, we show that only a few of the largest few Pauli error rates are needed and that a heuristic technique can complete the Pauli error distribution for ML decoding from this restricted dataset. Using these techniques, we demonstrate significant performance improvements for decoding quantum codes under a variety of physically relevant error models. For instance, with CER data that constitute merely 1% of the Pauli error rates in the system, we achieve a 10 times gain in performance compared with the case where decoding is based solely on the fidelity of the underlying noise process. Our conclusions underscore the promise of recent error characterization methods for improving quantum error correction and lowering overheads.

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Phys. Rev. Applied 25, 054065– Published 27 May, 2026
  • Received 23 July 2025
  • Revised 19 February 2026
  • Accepted 26 February 2026
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