Quantum multi-body problems using unsupervised machine learning
- Date
- November 5 (Wed) 15:00 - 16:00, 2025 (JST)
- Speaker
-
- Tomoya Naito (Project Assistant Professor, Department of Nuclear Engineering and Management, Graduate School of Engineering, The University of Tokyo)
- Venue
- Seminar Room #359 (Main Venue)
- via Zoom
- Language
- English
- Host
- Lingxiao Wang
I will introduce the recent development of a method to calculate the (anti)symmetrized wave functions and energies of the ground and low-lying excited states using the unsupervised machine learning technique. I will also introduce the recent attempts to consider the spin-isospin degrees of freedom and extend them to the Dirac equation.
References
- Tomoya Naito, Hisashi Naito, and Koji Hashimoto, Multi-body wave function of ground and low-lying excited states using unornamented deep neural networks, Phys. Rev. Research 5, 033189 (2023), doi: 10.1103/PhysRevResearch.5.033189, arXiv: 2302.08965
- Chuanxin Wang, Tomoya Naito, Jian Li, and Haozhao Liang, A neural network approach for two-body systems with spin and isospin degrees of freedom, arXiv: 2403.16819
- Chuanxin Wang, Tomoya Naito, Jian Li, and Haozhao Liang, A deep neural network approach to solve the Dirac equation, Eur. Phys. J. A 61, 162 (2025), doi: 10.1140/epja/s10050-025-01630-5, arXiv: 2412.03090
This is a closed event for scientists. Non-scientists are not allowed to attend. If you are not a member or related person and would like to attend, please contact us using the inquiry form. Please note that the event organizer or speaker must authorize your request to attend.