1. Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems
- Author
-
Nomura, Yusuke and Imada, Masatoshi
- Subjects
Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Disordered Systems and Neural Networks ,Condensed Matter - Superconductivity ,Physics - Computational Physics ,Quantum Physics - Abstract
With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation., Comment: Review paper, 12 pages, 12 figures
- Published
- 2024