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Learning Quantum Dissipation by the Neural Ordinary Differential Equation by Chen, Li; Wu, Yadong is a scholarly article available to read on EtoBox.

What is Learning Quantum Dissipation by the Neural Ordinary Differential Equation about?

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.

Author

Chen, Li; Wu, Yadong

Published

2022

Language

EN