Artículo
Exploring quantum localization with machine learning
Montes, Javier; Ermann, Leonardo
; Rivas, Alejandro Mariano Fidel
; Borondo, Florentino; Carlo, Gabriel Gustavo
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Fecha de publicación:
06/2024
Editorial:
Cornell University
Revista:
Arxiv
e-ISSN:
2331-8422
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom ”quantum” NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process.
Palabras clave:
Neural network
,
Quantum Localization
,
Machine Learning
,
Quantum chaos
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Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Montes, Javier; Ermann, Leonardo; Rivas, Alejandro Mariano Fidel; Borondo, Florentino; Carlo, Gabriel Gustavo; Exploring quantum localization with machine learning; Cornell University; Arxiv; 6-2024; 1-15
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