Quantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in quantum technologies. In recent years, there has been growing interest in leveraging neural networks to enhance the efficiency and accuracy of quantum state tomography. However, versatile methods that are broadly applicable across diverse reconstruction scenarios remain relatively underexplored. In this paper, we present two neural network-based reconstruction approaches for both pure and mixed quantum state tomography: Restricted Feature Based Neural Network and Mixed States Neural Network. By leveraging class information during reconstruction, we are able to achieve state-of-the-art performance of tomography for both pure and mixed quantum states.
@article{arxiv.2407.01734,
title = {Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches},
author = {Nhan Trong Luu and Tuyen Quang Nguyen and Duong Trung Luu and Thang Cong Truong},
journal= {arXiv preprint arXiv:2407.01734},
year = {2026}
}