Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Ray-based framework for state identification in quantum dot devices

Published

Author(s)

Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor

Abstract

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the "ray-based classification (RBC) framework," we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of image-based classification techniques from prior work, while reducing the number of measurement points needed by up to 70 %. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs. This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for nontraditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.
Citation
PRX Quantum
Volume
2
Issue
2

Keywords

semiconductor quantum computation, quantum dots, machine learning

Citation

Zwolak, J. , McJunkin, T. , Kalantre, S. , Neyens, S. , MacQuarrie, E. , Eriksson, M. and Taylor, J. (2021), Ray-based framework for state identification in quantum dot devices, PRX Quantum, [online], https://doi.org/10.1103/PRXQuantum.2.020335, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931926 (Accessed May 25, 2022)
Created June 7, 2021, Updated February 26, 2022