@inproceedings{43ce436a256a4bc8ab7cdb82d2dffebf,
title = "On how neural networks enhance quantum state tomography with limited resources",
abstract = "Quantum state tomography is defined as a process of reconstructing the density matrix of a quantum state and is an important task for various emerging quantum technologies. In this work, we propose a general quantum state tomography framework that employs deep neural networks to reconstruct quantum states from a set of measurements with high efficiency. In particular, we apply it to two cases, including few measurement copies and incomplete measurement. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve high fidelity for quantum state tomography when measurement resources are limited.",
author = "Hailan Ma and Daoyi Dong and Petersen, \{Ian R.\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",
year = "2021",
doi = "10.1109/CDC45484.2021.9683315",
language = "English",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4146--4151",
booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",
address = "United States",
}