Journal article
Efficiently measuring a quantum device using machine learning
- Abstract:
-
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...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Publisher copy:
- 10.1038/s41534-019-0193-4
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Authors
Bibliographic Details
- Publisher:
- Springer Nature Publisher's website
- Journal:
- npj Quantum Information Journal website
- Volume:
- 5
- Article number:
- 79
- Publication date:
- 2019-09-26
- Acceptance date:
- 2019-08-22
- DOI:
- EISSN:
-
2056-6387
- Source identifiers:
-
935453
Item Description
- Keywords:
- Pubs id:
-
pubs:935453
- UUID:
-
uuid:3f48fa13-0f72-41d0-b42d-e2341dfa5176
- Local pid:
- pubs:935453
- Deposit date:
- 2019-03-19
Terms of use
- Copyright holder:
- Lennon et al
- Copyright date:
- 2019
- Notes:
- Copyright © 2019 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. A correction to this article is available online from Springer Nature at: https://doi.org/10.1038/s41534-019-0214-3
- Licence:
- CC Attribution (CC BY)
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