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Robust Automated Recognition of Noisy Quantum Dot States

Published

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

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), Robust Automated Recognition of Noisy Quantum Dot States, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.17.024069, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932873 (Accessed August 18, 2022)
Created February 25, 2022, Updated February 26, 2022