Application of Machine Learning on Many-Body Problems
- Date
- August 23 (Mon) at 16:00 - 17:15, 2021 (JST)
- Speaker
-
- Daw-Wei Wang (Professor, Department of Physics, National Tsinghua University, Taiwan)
- Venue
- via Zoom
- Language
- English
Time: 4pm ~ 5:15pm (JST); 9am ~ 10:15am (CET); 3pm ~ 4:15pm (Taiwan)
In this talk, I will briefly introduce the application of machine learning methods on quantum many-body problems. It includes a self-supervised learning approach to decide the topological phase transition in the systems of ultracold atoms by using Time-of-Flight images only without knowing any priori knowledge [1]. We then develop the Random Sampling Neural Networks for the investigation of quantum many body ground state properties in the strong interacting regime by a model rtained in the weak interacting regime [2]. Finally, we provide an Quantum-Inspired-Recurrent Neural Network, which could give a precise long-time dynamics of a quantum many-body system, even the model is trained in the short-time regime. We hope to show the great possibility to use machine learning as a new tool to investigate the quantum many-body problems.
*Detailed information about the seminar refer to the email.
References
- Robust Identification of Topological Phase Transition by Self-Supervised Learning Approach, Chi-Ting Ho and Daw-Wei Wang, accepted in New J. Phys. 23 083021 (2021).
- Random Sampling Neural Network for Quantum Many-Body Problems, Chen-Yu Liu, Daw-Wei Wang, Phys. Rev. B 103, 205103 (2021).
