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Abstract
This paper presents a machine learning algorithm for automatically tuning semiconductor quantum dot devices. The algorithm efficiently searches a high-dimensional parameter space to find optimal operating conditions, significantly outperforming human experts in speed while achieving comparable performance. The algorithm is robust, adaptable to different materials and architectures, and provides a quantitative measure of device variability.
Publisher
Nature Communications
Published On
Aug 19, 2020
Authors
H. Moon, D. T. Lennon, J. Kirkpatrick, N. M. van Esbroeck, L. C. Camenzind, Liqui Yu, F. Vigneau, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, D. Sejdinović, E. A. Laird, N. Ares
Tags
machine learning
semiconductor
quantum dot
device tuning
algorithm
parameter optimization
variability

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