Adapting reservoir computing to solve the Schrodinger equation
Entity
UAM. Departamento de QuímicaPublisher
American Institute of PhysicsDate
2022-06-03Citation
10.1063/5.0087785
Chaos: An Interdisciplinary Journal of Nonlinear Science 32.6 (2022): 063111
ISSN
1054-1500 (print); 1089-7682 (online)DOI
10.1063/5.0087785Project
Gobierno de España. PGC2018-093854-B-I00Editor's Version
https://doi.org/10.1063/5.0087785Subjects
Echo State Network; Photonics; State Machine; QuímicaRights
© 2022 Author(s)Abstract
Reservoir computing is a machine learning algorithm that excels at predicting the evolution of time series, in particular, dynamical systems. Moreover, it has also shown superb performance at solving partial differential equations. In this work, we adapt this methodology to integrate the time-dependent Schrödinger equation, propagating an initial wavefunction in time. Since such wavefunctions are complex-valued high-dimensional arrays, the reservoir computing formalism needs to be extended to cope with complex-valued data. Furthermore, we propose a multi-step learning strategy that avoids overfitting the training data. We illustrate the performance of our adapted reservoir computing method by application to four standard problems in molecular vibrational dynamics
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Google Scholar:Domingo Colomer, Laia
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Borondo, J.
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Borondo, Florentino
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