Probing context-dependent errors in quantum processors

TL;DR

This paper introduces statistically rigorous methods to detect and quantify context-dependent errors in quantum processors, revealing how external factors influence quantum circuit outcomes and improving error modeling.

Contribution

The authors develop and demonstrate simple, widely applicable techniques for identifying context dependence in quantum circuit experiments, enhancing quantum error characterization.

Findings

Detected and quantified crosstalk on a 16-qubit device

Identified drift effects influencing quantum measurements

Methods integrate with standard quantum characterization tools

Abstract

Gates in error-prone quantum information processors are often modeled using sets of one- and two-qubit process matrices, the standard model of quantum errors. However, the results of quantum circuits on real processors often depend on additional external "context" variables. Such contexts may include the state of a spectator qubit, the time of data collection, or the temperature of control electronics. In this article we demonstrate a suite of simple, widely applicable, and statistically rigorous methods for detecting context dependence in quantum circuit experiments. They can be used on any data that comprise two or more "pools" of measurement results obtained by repeating the same set of quantum circuits in different contexts. These tools may be integrated seamlessly into standard quantum device characterization techniques, like randomized benchmarking or tomography. We experimentally demonstrate these methods by detecting and quantifying crosstalk and drift on the publicly accessible 16-qubit ibmqx3.