@inproceedings{4815fc80c24448538ed3ca91c1249175,
title = "Learning Quantum Distributions with Variational Diffusion Models",
abstract = "It is challenging to identify the state of many-body quantum systems, as recovering density matrices underlying the quantum state typically requires computational resources scale exponentially to the system size. In this work, we introduce the variational diffusion model (VDM) to efficiently learn high-dimensional quantum distributions with high fidelity, which is essential to realize the fast reconstruction of quantum states. We build up the VDM suitable for dealing with the high-dimensional quantum samples, and then perform numerical experiments to test our model and other autoregressive models, including recurrent neural network and transformer. It is found that the VDM can achieve a modest better performance with fewest parameters than other two to learn the distribution as desired. Our results pave the way to applying diffusion models to solve hard problems in the quantum domain.",
keywords = "Quantum state tomography, generative models, quantum distributions, variational diffusion model",
author = "Yong Wang and Shuming Cheng and Li Li and Jie Chen",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.095",
language = "英语",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "5888--5893",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
address = "荷兰",
edition = "2",
}