Neural networks for quantum state tomography with constrained measurements
Quantum Physics
2025-03-31 v3 Systems and Control
Systems and Control
Abstract
Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network(NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements.
Cite
@article{arxiv.2111.09504,
title = {Neural networks for quantum state tomography with constrained measurements},
author = {Hailan Ma and Daoyi Dong and Ian R. Petersen and Chang-Jiang Huang and Guo-Yong Xiang},
journal= {arXiv preprint arXiv:2111.09504},
year = {2025}
}