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- W4243598469 abstract "The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this Article, we employ nonparametric machine learning algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric spin-boson models. Our approach is not limited to spin-boson model and can be extended to complex systems." @default.
- W4243598469 created "2022-05-12" @default.
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- W4243598469 date "2021-08-13" @default.
- W4243598469 modified "2023-10-18" @default.
- W4243598469 title "Speeding up quantum dissipative dynamics of open systems with kernel methods" @default.
- W4243598469 doi "https://doi.org/10.33774/chemrxiv-2021-fgnrk" @default.
- W4243598469 hasPublicationYear "2021" @default.
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