Using reservoir computing to construct scarred wavefunctions

Fuente: arXiv
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Autores principales: Domingo, L., Borondo, J., Borondo, F.
Formato: Preprint
Publicado: 2024
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author Domingo, L.
Borondo, J.
Borondo, F.
author_facet Domingo, L.
Borondo, J.
Borondo, F.
contents Scar theory is one of the fundamental pillars in the field of quantum chaos, and scarred functions a superb tool to carry out studies in it. Several methods, usually semiclassical, have been described to cope with these two phenomena. In this paper, we present an alternative method, based on the novel machine learning algorithm known as Reservoir Computing, to calculate such scarred wavefunctions together with the associated eigenstates of the system. The resulting methodology achieves outstanding accuracy while reducing execution times by a factor of ten. As an illustration of the effectiveness of this method, we apply it to the widespread chaotic two-dimensional coupled quartic oscillator.
format Preprint
id arxiv_https___arxiv_org_abs_2401_10307
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Using reservoir computing to construct scarred wavefunctions
Domingo, L.
Borondo, J.
Borondo, F.
Quantum Physics
Chaotic Dynamics
Scar theory is one of the fundamental pillars in the field of quantum chaos, and scarred functions a superb tool to carry out studies in it. Several methods, usually semiclassical, have been described to cope with these two phenomena. In this paper, we present an alternative method, based on the novel machine learning algorithm known as Reservoir Computing, to calculate such scarred wavefunctions together with the associated eigenstates of the system. The resulting methodology achieves outstanding accuracy while reducing execution times by a factor of ten. As an illustration of the effectiveness of this method, we apply it to the widespread chaotic two-dimensional coupled quartic oscillator.
title Using reservoir computing to construct scarred wavefunctions
topic Quantum Physics
Chaotic Dynamics
url https://arxiv.org/abs/2401.10307