A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics

Exact numerical simulations of dynamics of open quantum systems often require immense computational resources. In this talk 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 (the spin-boson model) 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. In addition, it reduces the required computational resources for long-time simulations and holds the promise for becoming a valuable tool in the study of open quantum systems.