Exploring quantum localization with machine learning

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 convol...

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Bibliographic Details
Published inarXiv.org
Main Authors Montes, J, Ermann, Lenoardo, Rivas, Alejandro M F, Borondo, Florentino, Carlo, Gabriel G
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.06.2024
Subjects
Computational efficiency
Localization
Machine learning
Neural networks
Parameterization
Pattern recognition
Wave functions
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Summary: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
ISSN:2331-8422