Learning quantum dissipation by the neural ordinary differential equation

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, examining how to trace dissipation from observational data is important and may stimulate ways to suppress the dissipation. In this paper, we propose to learn about 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 helps to facilitate effective modeling and decoherence suppression in open quantum systems.