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arXiv 2001.044092020-01-13cond-mat.mes-hallcs.LGquant-ph

Quantum device fine-tuning using unsupervised embedding learning

University of Oxford（牛津大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Basel（巴塞尔大学）

N. M. van Esbroeck, D. T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L. C. Camenzind, L. Yu, D. M. Zumbühl, G. A. D. Briggs, D. Sejdinovic, N. Ares

DOI：: 10.1088/1367-2630/abb64c

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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 optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

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