Deep reinforcement learning for efficient measurement of quantum devices

Nguyen, V. and Orbell, S. B. and Lennon, Dominic T. and Moon, Hyungil and Vigneau, Florian and Camenzind, Leon C. and Yu, Liuqi and Zumbühl, Dominik M. and Briggs, G. Andrew D. and Osborne, Michael A.. (2021) Deep reinforcement learning for efficient measurement of quantum devices. npj Quantum Information, 7 (1). pp. 100-0.

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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 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 <30min, and sometimes as little as 1min. 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.
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:11 Apr 2022 12:55
Deposited On:11 Apr 2022 12:55

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