Speeding up quantum dissipative dynamics of open systems with kernel methods
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 ML algorithm (kernel walkkick ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-b