Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session X34: Computer-aided Tune-up and Calibration of Semiconductor Qubits
8:00 AM–11:00 AM,
Friday, March 8, 2019
BCEC
Room: 205A
Sponsoring
Unit:
DQI
Chair: Matthew Reed, HRL Laboratories, LLC
Abstract: X34.00005 : Efficiently measuring and tuning quantum devices using machine learning
10:24 AM–11:00 AM
View Presentation Abstract
Presenter:
Natalia Ares
(Materials, University of Oxford)
Author:
Natalia Ares
(Materials, University of Oxford)
I will present efficient measurements on a single quantum dot performed by a machine learning algorithm. This algorithm employs a probabilistic deep-generative model, capable of generating multiple full-resolution reconstructions from scattered partial measurements. Information theory is then used to select the most informative measurements to perform next. The algorithm outperforms standard grid scan techniques in different measurement configurations, reducing the number of measurements required by up to 4 times.
I will also show the use of Bayesian optimisation to tune a single quantum dot device. By generating a score function, we can make the algorithm find the operating regime of a device. We tune the device to the single-electron tunnelling regime searching in a high-dimensionality parameter space in less than a thousandth part of the time that it requires manually.
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