{"title":"短轨迹就是你所需要的:基于变压器的长时耗散量子动力学模型","authors":"Luis E. Herrera Rodríguez, Alexei A. Kananenka","doi":"arxiv-2409.11320","DOIUrl":null,"url":null,"abstract":"In this communication we demonstrate that a deep artificial neural network\nbased on a transformer architecture with self-attention layers can predict the\nlong-time population dynamics of a quantum system coupled to a dissipative\nenvironment provided that the short-time population dynamics of the system is\nknown. The transformer neural network model developed in this work predicts the\nlong-time dynamics of spin-boson model efficiently and very accurately across\ndifferent regimes, from weak system-bath coupling to strong coupling\nnon-Markovian regimes. Our model is more accurate than classical forecasting\nmodels, such as recurrent neural networks and is comparable to the\nstate-of-the-art models for simulating the dynamics of quantum dissipative\nsystems, based on kernel ridge regression.","PeriodicalId":501226,"journal":{"name":"arXiv - PHYS - Quantum Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics\",\"authors\":\"Luis E. Herrera Rodríguez, Alexei A. Kananenka\",\"doi\":\"arxiv-2409.11320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this communication we demonstrate that a deep artificial neural network\\nbased on a transformer architecture with self-attention layers can predict the\\nlong-time population dynamics of a quantum system coupled to a dissipative\\nenvironment provided that the short-time population dynamics of the system is\\nknown. The transformer neural network model developed in this work predicts the\\nlong-time dynamics of spin-boson model efficiently and very accurately across\\ndifferent regimes, from weak system-bath coupling to strong coupling\\nnon-Markovian regimes. Our model is more accurate than classical forecasting\\nmodels, such as recurrent neural networks and is comparable to the\\nstate-of-the-art models for simulating the dynamics of quantum dissipative\\nsystems, based on kernel ridge regression.\",\"PeriodicalId\":501226,\"journal\":{\"name\":\"arXiv - PHYS - Quantum Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Quantum Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Quantum Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics
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.