Optimal noise estimation from syndrome statistics of quantum codes

TL;DR

This paper develops a general framework for estimating noise models from syndrome measurements in quantum error correction, providing conditions for identifiability, practical estimation methods, and demonstrating optimality and applicability during device operation.

Contribution

It introduces a general condition for noise model identifiability from syndrome data and proposes an efficient, optimal estimation method applicable to various stabilizer codes.

Findings

The estimation method reaches the Cramér-Rao Bound.

It outperforms existing methods in accuracy and runtime.

The approach is applicable during quantum device operation.

Abstract

Quantum error correction allows to actively correct errors occurring in a quantum computation when the noise is weak enough. To make this error correction competitive information about the specific noise is required. Traditionally, this information is obtained by benchmarking the device before operation. We address the question of what can be learned from only the measurements done during decoding. Such estimation of noise models was proposed for surface codes, exploiting their special structure, and in the limit of low error rates also for other codes. However, so far it has been unclear under what general conditions noise models can be estimated from the syndrome measurements. In this work, we derive a general condition for identifiability of the error rates. For general stabilizer codes, we prove identifiability under the assumption that the rates are small enough. Without this assumption we prove a result for perfect codes. Finally, we propose a practical estimation method with linear runtime for concatenated codes. We demonstrate that it outperforms other recently proposed methods and that the estimation is optimal in the sense that it reaches the Cram\'{e}r-Rao Bound. Our method paves the way for practical calibration of error corrected quantum devices during operation.