Exploring quantum localization with machine learning

Autor:	Montes, J., Ermann, Lenoardo, Rivas, Alejandro M. F., Borondo, Florentino, Carlo, Gabriel G.
Rok vydání:	2024
Předmět:	Quantum Physics Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	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 Comment: 15 pages, 12 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.00363 Zobrazit plný text záznamu View this record from Arxiv