Learning quantum dissipation by the neural ordinary differential equation

TL;DR

This paper introduces a neural ordinary differential equation approach to learn quantum dissipation from observational data, demonstrating its effectiveness on quantum-spin systems and providing insights into data efficiency for experimental applications.

Contribution

It presents a novel method to infer quantum dissipation dynamics directly from data using neural ODEs, aiding in decoherence understanding and suppression.

Findings

Successfully applied to large spin and spin-1/2 chain systems.

Provides guidance on data requirements for effective learning.

Facilitates modeling and potential decoherence control in quantum systems.

Abstract

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.