Fast Bayesian tomography of a two-qubit gate set in silicon

TL;DR

This paper presents a rapid Bayesian method for self-consistent quantum process tomography, demonstrated on silicon-based two-qubit gates, enabling real-time, adaptive characterization leveraging prior information.

Contribution

Introduction of fast Bayesian tomography (FBT), a novel adaptive self-consistent process tomography method that improves speed and robustness in quantum gate characterization.

Findings

FBT provides real-time estimates during data acquisition.

FBT leverages prior information from benchmarking.

Demonstrated on silicon two-qubit gates with high accuracy.

Abstract

Benchmarking and characterising quantum states and logic gates is essential in the development of devices for quantum computing. We introduce a Bayesian approach to self-consistent process tomography, called fast Bayesian tomography (FBT), and experimentally demonstrate its performance in characterising a two-qubit gate set on a silicon-based spin qubit device. FBT is built on an adaptive self-consistent linearisation that is robust to model approximation errors. Our method offers several advantages over other self-consistent tomographic methods. Most notably, FBT can leverage prior information from randomised benchmarking (or other characterisation measurements), and can be performed in real time, providing continuously updated estimates of full process matrices while data is acquired.