Work

Exploring quantum localization with machine learning

Year: 2024

Type: article

Abstract: We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization (probability concentration) in a specific region of the quantum phase space. 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 –i.e. at large Hilbert space dimensions N = ( 2 π ħ ) − 1 – 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.

Source: Physica A Statistical Mechanics and its Applications

DOI: 10.1016/j.physa.2024.130310

Authors: J. Montes, Leonardo Ermann, Alejandro M.F. Rivas, F. Borondo, Gabriel G. Carlo

Institutions: Universidad Carlos III de Madrid, Consejo Nacional de Investigaciones Científicas y Técnicas, Comisión Nacional de Energía Atómica, Universidad Autónoma de Madrid

Language: English

Topic: Quantum chaos and dynamical systems

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,450

Funders: Ministerio de Ciencia, Innovación y Universidades, Consejo Nacional de Investigaciones Científicas y Técnicas