Deep reinforcement learning for efficient measurement of quantum devices
Abstract 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 p...
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Nature Portfolio
2021
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oai:doaj.org-article:e474432013bf4457b5b6d76522722bc92021-12-02T17:41:10ZDeep reinforcement learning for efficient measurement of quantum devices10.1038/s41534-021-00434-x2056-6387https://doaj.org/article/e474432013bf4457b5b6d76522722bc92021-06-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00434-xhttps://doaj.org/toc/2056-6387Abstract 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 transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.V. NguyenS. B. OrbellD. T. LennonH. MoonF. VigneauL. C. CamenzindL. YuD. M. ZumbühlG. A. D. BriggsM. A. OsborneD. SejdinovicN. AresNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-9 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 |
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Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 V. Nguyen S. B. Orbell D. T. Lennon H. Moon F. Vigneau L. C. Camenzind L. Yu D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares Deep reinforcement learning for efficient measurement of quantum devices |
| description |
Abstract 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 transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices. |
| format |
article |
| author |
V. Nguyen S. B. Orbell D. T. Lennon H. Moon F. Vigneau L. C. Camenzind L. Yu D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares |
| author_facet |
V. Nguyen S. B. Orbell D. T. Lennon H. Moon F. Vigneau L. C. Camenzind L. Yu D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares |
| author_sort |
V. Nguyen |
| title |
Deep reinforcement learning for efficient measurement of quantum devices |
| title_short |
Deep reinforcement learning for efficient measurement of quantum devices |
| title_full |
Deep reinforcement learning for efficient measurement of quantum devices |
| title_fullStr |
Deep reinforcement learning for efficient measurement of quantum devices |
| title_full_unstemmed |
Deep reinforcement learning for efficient measurement of quantum devices |
| title_sort |
deep reinforcement learning for efficient measurement of quantum devices |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/e474432013bf4457b5b6d76522722bc9 |
| work_keys_str_mv |
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