Speeding up quantum dissipative dynamics of open systems with kernel methods

27 September 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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.


machine learning
artificial intelligence
quantum dynamics
kernel method
kernel ridge regression
open quantum system
quantum dissipative dynamics
spin-boson model
hierarchical equations of motion


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