Ma, HailanDong, DaoyiPetersen, Ian R.Huang, Chang JiangXiang, Guo Yong2025-05-232025-05-231570-0755ORCID:/0000-0002-7425-3559/work/183659479http://www.scopus.com/inward/record.url?scp=85203384978&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733752856Quantum 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.We thank Zhi-Bo Hou for useful discussions. This work was supported by the Australian Research Council\u2019s Future Fellowship funding scheme under Project FT220100656, the Australian Research Council\u2019s Discovery Projects funding scheme DP210101938, and the U.S. Office of Naval Research Global under Grant N62909-19-1-2129.16en© The Author(s) 2024.ErrorsNeural networksQuantum state tomographyRobustness2024-09-0910.1007/s11128-024-04522-785203384978