Estimating and decoding coherent errors of QEC experiments with detector error models

TL;DR

This paper introduces a method to detect and estimate coherent errors in quantum error correction experiments using syndrome history, eliminating the need for prior device benchmarking and improving error modeling accuracy.

Contribution

The authors demonstrate that detector error models derived from syndrome data can effectively characterize both stochastic and coherent noise without additional benchmarking.

Findings

Detector error models work well for both noise regimes

Coherent errors cause interference effects in error rates

Decoding with coherent noise models alters error thresholds

Abstract

Decoders of quantum error correction (QEC) experiments make decisions based on detected errors and the expected rates of error events, which together comprise a detector error model. Here we show that the syndrome history of QEC experiments is sufficient to detect and estimate coherent errors, removing the need for prior device benchmarking experiments. Importantly, our method shows that experimentally determined detector error models work equally well for both stochastic and coherent noise regimes. We model fully-coherent or fully-stochastic noise for repetition and surface codes and for various phenomenological and circuit-level noise scenarios, by employing Majorana and Monte Carlo simulators. We capture the interference of coherent errors, which appears as enhanced or suppressed physical error rates compared to the stochastic case, and also observe hyperedges that do not appear in the corresponding Pauli-twirled models. Finally, we decode the detector error models undergoing coherent noise and find different thresholds compared to detector error models built based on the stochastic noise assumption.