Dissemin is shutting down on January 1st, 2025

Published in

IOP Publishing, New Journal of Physics, 9(22), p. 095003, 2020

DOI: 10.1088/1367-2630/abb64c

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Quantum device fine-tuning using unsupervised embedding learning

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Postprint: archiving allowed
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Data provided by SHERPA/RoMEO

Abstract

Abstract 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.