Machine learning enables completely automatic tuning of a quantum device faster than human experts

Moon, H. and Lennon, D. T. and Kirkpatrick, J. and van Esbroeck, N. M. and Camenzind, L. C. and Yu, Liuqi and Vigneau, F. and Zumbühl, D. M. and Briggs, G. A. D. and Osborne, M. A. and Sejdinovic, D. and Laird, E. A. and Ares, N.. (2020) Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications, 11. p. 4161.

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Official URL: https://edoc.unibas.ch/81974/

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Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Experimentalphysik Quantenphysik (Zumbühl)
UniBasel Contributors:Zumbühl, Dominik M
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature Publishing Group
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:12 Apr 2021 09:42
Deposited On:12 Apr 2021 09:42

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