Convolutional neural networks for long-time dissipative quantum dynamics

Autor:	Rodriguez, Luis E. Herrera, Kananenka, Alexei A.
Rok vydání:	2020
Předmět:	Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	Exact numerical simulations of dynamics of open quantum systems often require immense computational resources. We demonstrate that a deep artificial neural network comprised of convolutional layers is a powerful tool for predicting long-time dynamics of an open quantum system provided the preceding short-time dynamics of the system is known. The neural network model developed in this work simulates long-time dynamics efficiently and very accurately across different dynamical regimes from weakly damped coherent motion to incoherent relaxation. The model was trained on a data set relevant to photosynthetic excitation energy transfer and can be deployed to study long-lasting quantum coherence phenomena observed in light-harvesting complexes. Furthermore, our model performs well for the initial conditions different than those used in the training. Our approach considerably reduces the required computational resources for long-time simulations and holds promise for becoming a valuable tool in the study of open quantum systems. Comment: 9 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.11009 Zobrazit plný text záznamu View this record from Arxiv