Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics-Reference-Cited by-同舟云学术

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

Published:2022 Issue:39 Volume:24 Page:24012-24020
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Gao Yuanqi¹^ORCID,Wang Xian²^ORCID,Yu Nanpeng¹^ORCID,Wong Bryan M.³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of California-Riverside, Riverside, CA, USA

2. Department of Physics and Astronomy, University of California-Riverside, Riverside, CA, USA

3. Department of Chemical and Environmental Engineering, Materials Science and Engineering Program, Department of Chemistry, and Department of Physics and Astronomy, University of California-Riverside, Riverside, CA, USA

Abstract

Deep reinforcement learning can be used as an efficient artificial intelligence approach to control time-dependent quantum dynamical systems.

Funder

Basic Energy Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP02495K

Reference49 articles.

1. Quantum optimal control with quantum computers: A hybrid algorithm featuring machine learning optimization

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5. Magnetic Trapping and Coherent Control of Laser-Cooled Molecules

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