Journal article
Deep reinforcement learning for efficient measurement of quantum devices
- Abstract:
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Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 3.7MB)
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- Publisher copy:
- 10.1038/s41534-021-00434-x
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Authors
Bibliographic Details
- Publisher:
- Springer Nature Publisher's website
- Journal:
- npj Quantum Information Journal website
- Volume:
- 7
- Issue:
- 1
- Article number:
- 100
- Publication date:
- 2021-06-18
- Acceptance date:
- 2021-05-24
- DOI:
- EISSN:
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2056-6387
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1183423
- Local pid:
- pubs:1183423
- Deposit date:
- 2021-07-08
Terms of use
- Copyright holder:
- Nguyen et al.
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021. Open Access: 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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