Issue 39, 2022

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

Abstract

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

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

Supplementary files

Article information

Article type
Paper
Submitted
01 Jun 2022
Accepted
09 Sep 2022
First published
13 Sep 2022

Phys. Chem. Chem. Phys., 2022,24, 24012-24020

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

Y. Gao, X. Wang, N. Yu and B. M. Wong, Phys. Chem. Chem. Phys., 2022, 24, 24012 DOI: 10.1039/D2CP02495K

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