Scalable linearized gate set tomography

TL;DR

This paper introduces a scalable extension to linearized gate set tomography, enabling efficient and accurate characterization of errors in many-qubit quantum computers using shallow circuit data.

Contribution

The authors develop a scalable method for linearized gate set tomography that handles many-qubit systems with sparse error models and shallow circuits.

Findings

Accurately characterizes errors in simulated ten-qubit systems.

Robust against errors outside the sparse error model.

Effective for systems with coherent and stochastic errors, including crosstalk.

Abstract

Characterizing errors on many-qubit quantum computers remains a key challenge to understanding and improving the performance of these devices. Current characterization methods either don't scale beyond a few qubits, or make simplifying assumptions (such as assuming stochastic Pauli error) that obscure the underlying physical error mechanisms. In this work, we present a scalable extension to gate set tomography-linearized gate set tomography-that enables characterization of many-qubit systems. Linearized gate set tomography relies on sparse error models, a linear approximation to enable efficient data fitting, and data from shallow circuits-so that the systematic error in the linear approximation is small. We demonstrate the accuracy of our technique using simulations of a ten-qubit system with coherent and stochastic errors, including coherent crosstalk, and we demonstrate that it is robust in presence of additional errors that are not included within the sparse error model ansatz.