Calibration of syndrome measurements in a single experiment

TL;DR

This paper introduces a calibration method for syndrome measurements in quantum error correction that efficiently accounts for measurement noise using data from a single experiment, improving accuracy and reducing effort.

Contribution

The authors present a novel calibration technique that extracts syndrome statistics from a single experiment, enabling more accurate noise estimation in quantum systems.

Findings

Effective calibration of syndrome measurements demonstrated on IBM quantum computer.

Method reduces experimental effort compared to traditional calibration techniques.

Improves accuracy of quantum error correction by accounting for measurement noise.

Abstract

Quantum error correction can reduce the effects of noise in quantum systems, e.g. in metrology or most notably in quantum computing. Typically, this requires making measurements that provide information about the errors that have occurred in the system. However, these syndrome measurements themselves introduce noise into the system, for example by using noisy gates. A complete characterization of the measurements is very costly. Here we describe a calibration method to obtain the syndrome statistics taking into account the additional noise sources. All calibration data are extracted from a single experiment in which the syndrome measurement is performed twice in a row. Thus, our method allows an accurate evaluation of syndrome measurements with significantly less effort than existing methods. We give examples of the application of this method to noise estimation and error correction. Finally, we discuss the results of experiments performed on an IBM quantum computer.