Efficient diagnostics for quantum error correction

TL;DR

This paper introduces a scalable experimental method using Pauli error reconstruction to better predict logical performance in quantum error correction, outperforming standard metrics and aiding in error correction scheme selection.

Contribution

The paper presents a novel, scalable approach for predicting quantum error correction performance that surpasses traditional error metrics, with practical experimental validation.

Findings

Outperforms standard error metrics in predicting logical performance

Effective with limited data across various error models

Assists in selecting optimal error correction schemes

Abstract

Fault-tolerant quantum computing will require accurate estimates of the resource overhead, but standard metrics such as gate fidelity and diamond distance have been shown to be poor predictors of logical performance. We present a scalable experimental approach based on Pauli error reconstruction to predict the performance of concatenated codes. Numerical evidence demonstrates that our method significantly outperforms predictions based on standard error metrics for various error models, even with limited data. We illustrate how this method assists in the selection of error correction schemes.