⟨Math┃NCTS┃Phys⟩
Machine Learning Initiative
⟨Math┃NCTS┃Phys⟩
Machine Learning Initiative
Date: March 5th, 2022.
Venue: NCTS, Math Division.
Date: 3/21/2022
Time: 12:00-13:00 (Taipei time)
Venue: Zoom
Speaker: Daw-wei Wang (Phys, NTHU)
Title: Exploring Quantum Many-Body Problems by Random Sampling Neural Networks and Self-Supervised Learning
Abstract
The eigenvalue problem and the ground state properties of quantum many-body systems is a fundamental and challenging subject in condensed-matter physics because the dimension of the Hilbert space grows exponentially as the system size increases. Here we propose a general numerical method, random sampling neural networks (RSNNs), to utilize the pattern recognition technique for the random sampling matrix elements of an interacting many-body system via a self-supervised learning approach. Several exactly solvable one-dimensional models are tested with pretty high accuracy (>96%) for the energy spectra, magnetization, and critical exponents, etc. in the strongly correlated regime [1]. We further apply such a self-supervised learning method for the identification of topological phase transitions using time-of-fight images in ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is sensitive to the training region [2]. our results demonstrate a robust identification in various 1D and 2D exactly solvable models. As a result, our self-supervised approach should be a very general and reliable method for quantum many-body systems even without a priori knowledge of the solution. If time is allowed, we will present our recent works for the application in long-time dynamics of a quantum many-body system.
[1] Chen-Yu Liu, Daw-Wei Wang, Phys. Rev. B 103, 205103 (2021).
[2] Chi-Ting Ho and Daw-Wei Wang, New J. Phys. 23 083021 (2021).
Date: 4/12/2022
Time: 12:10-13:00 (Taipei time)
Venue: Webex Meeting number (access code): 2523 568 3718 Meeting password: JAuUTeJe342
Speaker: Cheng-Fang Su (National Yang MIng Chiao Tung University)
Title: Quantum Variational Algorithms and Their Application in Biomedicine
Abstract
This talk will briefly introduce the basic knowledge of quantum computers and their application in biomedicine and illustrate the importance of quantum algorithms for developing related scientific fields. For example, the application of quantum variational algorithms in virus mutation research, vaccine, and drug design. This related work about quantum variational algorithms is joint with Professor Chi-Chuan Hwang.
Organizers:
Co-Chair: Chen, Pochung 陳柏中 (Department of Physics, NTHU)
Co-Chair: Huang, Tsung-Ming 黃聰明 (Department of Mathematics, NTNU)
Chen, Jiunn-Wei 陳俊瑋 (Department of Physics, NTU)
Giataganas, Dimitrios (Department of Physics, NSYSU)
Hu, Wei-Fan 胡偉帆 (Department of Mathematics, NCU)
Shiue, Ming-Cheng 薛名成 (Department of Applied Mathematics, NYCU)
Wang, Weichung 王偉仲 (Institute of Applied Mathematical Sciences, NTU)