Efficiently measuring a quantum device using machine learning

Lennon, D. T. and Moon, H. and Camenzind, L. C. and Yu, Liuqi and Zumb`uhl, D. M. and Briggs, G. A. D. and Osborne, M. A. and Laird, E. A. and Ares, N.. (2019) Efficiently measuring a quantum device using machine learning. npj Quantum Information, 5. p. 79.

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Official URL: https://edoc.unibas.ch/75219/

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Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.
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:Nature Publishing Group
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:21 Apr 2020 09:16
Deposited On:21 Apr 2020 09:16

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