Document Type
Article
Publication Date
8-27-2020
Publication Title
Physical Review Research
Volume
2
Issue
3
First page number:
1
Last page number:
6
Abstract
Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is often difficult to assess because they depend on the Hamiltonian parameters and the particular observable under study. Here, we illustrate a general, system- and approximation-independent, approach to improve the accuracy of quantum dynamics approximations. The method is based on a Bayesian machine learning (BML) algorithm that is trained by a small number of exact results and a large number of approximate calculations, resulting in ML models that can generalize exact quantum results to different dynamical processes. Thus, a ML model trained by a combination of approximate and rigorous results for a certain inelastic transition can make accurate predictions for different transitions without rigorous calculations. This opens the possibility of improving the accuracy of approximate calculations for quantum transitions that are out of reach of exact scattering theory.
Disciplines
Quantum Physics | Theory and Algorithms
File Format
File Size
381 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Jasinski, A.,
Montaner, J.,
Forrey, R. C.,
Yang, B. H.,
Stancil, P. C.,
Balakrishnan, N.,
Dai, J.,
Vargas-Hernandez, A.,
Krems, R. V.
(2020).
Machine Learning Corrected Quantum Dynamics Calculations.
Physical Review Research, 2(3),
1-6.
http://dx.doi.org/10.1103/PhysRevResearch.2.032051