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.
Learning logical Pauli noise in quantum error correction
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
The characterization of quantum devices is crucial for their practical implementation but can be costly in experimental effort and classical postprocessing. Therefore, it is desirable to measure only the information that is relevant for specific applications and develop protocols that require little additional effort. In this work, we focus on the characterization of quantum computers in the context of stabilizer quantum error correction. For arbitrary stabilizer codes, subsystem codes, and data syndrome codes, we prove that the logical error channel induced by Pauli noise can be estimated from syndrome data under minimal conditions. More precisely, for any such code, we show that the estimation is possible as long as the code can correct the noise.
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quant-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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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.