Toward Robust Autotuning of Noisy Quantum Dot Devices

Published

February 25, 2022

Author(s)

Joshua Ziegler, Thomas McJunkin, Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor, Justyna Zwolak

Abstract

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a "gatekeeper" system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

Citation

Physical Review Applied

Volume

17

Issue

2

Pub Type

Journals

Download Paper

https://doi.org/10.1103/PhysRevApplied.17.024069

Local Download

Keywords

machine learning, quantum dots, autonomous control

Citation

Ziegler, J. , McJunkin, T. , Joseph, E. , Kalantre, S. , Harpt, B. , Savage, D. , Lagally, M. , Eriksson, M. , Taylor, J. and Zwolak, J. (2022), Toward Robust Autotuning of Noisy Quantum Dot Devices, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.17.024069, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932873 (Accessed December 3, 2024)