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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2409.11320 |
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| _version_ | 1866929542822625280 |
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| author | Rodríguez, Luis E. Herrera Kananenka, Alexei A. |
| author_facet | Rodríguez, Luis E. Herrera Kananenka, Alexei A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics Rodríguez, Luis E. Herrera Kananenka, Alexei A. Quantum Physics 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. |
| title | A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.11320 |