التفاصيل البيبلوغرافية
| العنوان: |
A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics. |
| المؤلفون: |
Herrera Rodríguez, Luis E.1 (AUTHOR), Kananenka, Alexei A.1 (AUTHOR) akanane@udel.edu |
| المصدر: |
Journal of Chemical Physics. 11/7/2024, Vol. 161 Issue 17, p1-8. 8p. |
| مصطلحات موضوعية: |
*ARTIFICIAL neural networks, *QUANTUM theory, *TRANSFORMER models, *POPULATION dynamics, *SYSTEM dynamics, *RECURRENT neural networks |
| مستخلص: |
In this Communication, we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system–bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks, and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems based on kernel ridge regression. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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