  Exact numerical simulations of dynamics of open quantum systems often require
immense computational resources. We demonstrate that a deep artificial neural
network comprised of convolutional layers is a powerful tool for predicting
long-time dynamics of an open quantum system provided the preceding short-time
dynamics of the system is known. The neural network model developed in this
work simulates long-time dynamics efficiently and very accurately across
different dynamical regimes from weakly damped coherent motion to incoherent
relaxation. The model was trained on a data set relevant to photosynthetic
excitation energy transfer and can be deployed to study long-lasting quantum
coherence phenomena observed in light-harvesting complexes. Furthermore, our
model performs well for the initial conditions different than those used in the
training. Our approach considerably reduces the required computational
resources for long-time simulations and holds promise for becoming a valuable
tool in the study of open quantum systems.
