  Quantum dissipation arises from the unavoidable coupling between a quantum
system and its surrounding environment, which is known as a major obstacle in
the quantum processing of information. Apart from its existence, how to trace
the dissipation from observational data is a crucial topic that may stimulate
manners to suppress the dissipation. In this paper, we propose to learn the
quantum dissipation from dynamical observations using the neural ordinary
differential equation, and then demonstrate this method concretely on two open
quantum-spin systems -- a large spin system and a spin-1/2 chain. We also
investigate the learning efficiency of the dataset, which provides useful
guidance for data acquisition in experiments. Our work promisingly facilitates
effective modeling and decoherence suppression in open quantum systems.
