van de Walle, Anka and Schmitt, Markus and Bohrdt, Annabelle (2025) Many-body dynamics with explicitly time-dependent neural quantum states. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 6 (4): 045011. ISSN , 2632-2153
Full text not available from this repository. (Request a copy)Abstract
Simulating the dynamics of many-body quantum systems is a significant challenge, especially in higher dimensions where entanglement grows rapidly. Neural quantum states (NQS) offer a promising tool for representing quantum wavefunctions, but their application to time evolution faces scaling challenges. We introduce the time-dependent neural quantum state (t-NQS), a novel approach incorporating explicit time dependence into the neural network ansatz. This framework optimizes a single, time-independent set of parameters to solve the time-dependent Schr & ouml;dinger equation across an entire time interval. We detail an autoregressive, attention-based transformer architecture and techniques for extending the model's applicability. To benchmark and demonstrate our method, we simulate quench dynamics in the 2D transverse field Ising model and the time-dependent preparation of the 2D antiferromagnetic state in a Heisenberg model, demonstrating state of the art performance, scalability, and extrapolation to unseen intervals. These results establish t-NQS as a powerful framework for exploring quantum dynamics in strongly correlated systems.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LOCALIZATION; SIMULATOR; HUNDREDS; strongly correlated electrons; machine learning; neural quantum states; quench dynamics; quantum many-body physics; transformer |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Physics > Institute of Theroretical Physics |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 02 Jun 2026 11:31 |
| Last Modified: | 02 Jun 2026 11:31 |
| URI: | https://pred.uni-regensburg.de/id/eprint/67390 |
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