Quantum device fine-tuning using unsupervised embedding learning

Quantum device fine-tuning using unsupervised embedding learning

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IOP Publishing 2020-01-01
Series:New Journal of Physics
Subjects:
quantum devices
machine learning
automatic tuning
variational auto-encoder
Online Access:https://doi.org/10.1088/1367-2630/abb64c
Description
Summary: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.
ISSN:1367-2630

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