Quantum device fine-tuning using unsupervised embedding learning

van Esbroeck, N. M. and Lennon, D. T. and Moon, H. and Nguyen, V. and Vigneau, F. and Camenzind, L. C. and Yu, L. and Zumbühl, D. M. and Briggs, G. A. D. and Sejdinovic, D. and Ares, N.. (2020) Quantum device fine-tuning using unsupervised embedding learning. New Journal of Physics, 22 (9). 095003.

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Official URL: https://edoc.unibas.ch/81972/

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Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.
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:IOP Publishing
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:12 Apr 2021 12:10
Deposited On:12 Apr 2021 12:10

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