Artículos de revistas
Estimating the degree of non-Markovianity using machine learning
Fecha
2021-02-01Registro en:
Physical Review A, v. 103, n. 2, 2021.
2469-9934
2469-9926
10.1103/PhysRevA.103.022425
2-s2.0-85101763185
7226048122013565
0000-0001-9432-1603
Autor
Universidade Estadual Paulista (Unesp)
İzmir University of Economics
Universidad Mayor
Institución
Resumen
In the last few years, the application of machine learning methods has become increasingly relevant in different fields of physics. One of the most significant subjects in the theory of open quantum systems is the study of the characterization of non-Markovian memory effects that emerge dynamically throughout the time evolution of open systems as they interact with their surrounding environment. Here we consider two well-established quantifiers of the degree of memory effects, namely, the trace distance and the entanglement-based measures of non-Markovianity. We demonstrate that using machine learning techniques, in particular, support vector machine algorithms, it is possible to estimate the degree of non-Markovianity in two paradigmatic open system models with high precision. Our approach can be experimentally feasible to estimate the degree of non-Markovianity, since it requires a single or at most two rounds of state tomography.