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Improving error suppression with noise-aware decoding

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arxiv 2502.21044 v2 pith:ZJ5XYYFV submitted 2025-02-28 quant-ph

Evan T. Hockings , Andrew C. Doherty , Robin Harper This is my paper · ORCID
classification quant-ph
keywords errordecodingnoise-awarequantumnoisecalibrationcharacterisationcode
verification ladder T0 review T1 audit T2 compute T3 formal
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We demonstrate that the performance of quantum error correction can be improved with noise-aware decoders that are calibrated to the likelihood of physical error configurations in a device. We show that noise-aware decoding increases the error suppression factor of the surface code, yielding reductions in the logical error rate that increase exponentially with the code distance. Our calibration protocol involves circuit-level Pauli noise characterisation experiments with averaged circuit eigenvalue sampling. This enables decoder calibration at the scales required for fault-tolerant quantum computation and near-optimal decoding when compared to the true noise model. Our results indicate that these noise characterisation experiments could be performed and processed in seconds for superconducting quantum computers. This establishes the practicality and utility of noise-aware decoding for quantum error correction at scale.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reinforcement Learning Control of Quantum Error Correction

    quant-ph 2025-11 conditional novelty 7.0 of 10

    A reinforcement-learning controller that treats quantum error-detection events as rewards stabilizes a superconducting surface/color code under injected drift, cuts logical error rates ~20% after expert calibration, a...

  2. Enhancing Decoding Performance using Efficient Error Learning

    quant-ph 2025-07 conditional novelty 6.0 of 10

    Using about 1% of Pauli error rates from Cycle Error Reconstruction plus a heuristic completion, maximum-likelihood decoding of concatenated Steane codes achieves roughly 5 to 10 times lower logical error rates than f...

  3. QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A continually adapted neural pre-decoder reduces logical error rate and residual matching latency versus a fixed neural baseline across 110 OOD noise settings and zero-shot on Willow.

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