Efficiently measuring a quantum device using machine learning

TL;DR

This paper introduces a machine learning-based method to automate and optimize measurements on quantum devices, significantly reducing measurement time and effort, and paving the way for automated control of large quantum systems.

Contribution

It presents a novel machine learning algorithm that automates quantum measurements using information theory and probabilistic models, outperforming traditional grid scan methods.

Findings

Reduced measurement count by up to 4 times

Decreased measurement time by 3.7 times

Demonstrated effectiveness on different quantum measurement configurations

Abstract

Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of automation. We present measurements on a quantum dot device performed by a machine learning algorithm. The algorithm selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model, the latter capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead presents the use of algorithms to automate measurement. This work lays the foundation for automated control of large quantum circuits.