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## Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

preprint

submitted on 02.09.2020 and posted on 02.09.2020 by Xian Wang, Anshuman Kumar, Christian Shelton, Bryan Wong#### preprint

Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields,

*E(t)*, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of*E(t)*), and (2) a cross-correlation neural network approach for directly predicting*E(t)*in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.#### Read the published paper

in Physical Chemistry Chemical Physics

### Categories

### Funding

### National Science Foundation Grant No. CBET-1833218

### National Science Foundation Grant No. CHE-1808242

### History

#### Email Address of Submitting Author

usagi@alum.mit.edu#### Institution

University of California, Riverside#### Country

United States#### ORCID For Submitting Author

0000-0002-3477-8043#### Declaration of Conflict of Interest

no conflict of interest### Licence

### Exports

#### Read the published paper

in Physical Chemistry Chemical Physics