Detector error model probabilities can be recovered from syndrome histories through a Walsh-Hadamard transform, giving a closed-form inverse for individual and aggregated DEM events.
Optimization of decoder priors for accurate quantum error correction
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abstract
Accurate decoding of quantum error-correcting codes is a crucial ingredient in protecting quantum information from decoherence. It requires characterizing the error channels corrupting the logical quantum state and providing this information as a prior to the decoder. We introduce a reinforcement learning inspired method for calibrating these priors that aims to minimize the logical error rate. Our method significantly improves the decoding accuracy in repetition and surface code memory experiments executed on Google's Sycamore processor, outperforming the leading decoder-agnostic method by 16% and 3.3% respectively. This calibration approach will serve as an important tool for maximizing the performance of both near-term and future error-corrected quantum devices.
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quant-ph 1years
2025 1verdicts
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Estimating detector error models from syndrome data
Detector error model probabilities can be recovered from syndrome histories through a Walsh-Hadamard transform, giving a closed-form inverse for individual and aggregated DEM events.