Ma, HailanXiao, ShuixinDong, DaoyiPetersen, Ian R.2026-07-032026-07-039781713872344ORCID:/0000-0002-7425-3559/work/219177091ORCID:/0000-0003-4856-9450/work/219177599https://hdl.handle.net/1885/733812329Quantum detector tomography is a fundamental technique for calibrating quantum devices and thus lay foundations for quantum information processing tasks. In this work, we propose a quantum detector tomography method that employs deep neural networks to reconstruct quantum detectors from a set of probe states with high efficiency. Numerical results demonstrate that the proposed method exhibits a significant potential to estimate phase-insensitive detectors.This work was supported by the Australian Research Council's Future Fellowship funding scheme under Project FT220100656, and U.S. Office of Naval Research Global under Grant N62909-19-1-2129. Hailan Ma would like to thank Yuanlong Wang for helpful discussions.6enPublisher Copyright: Copyright © 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/)estimationneural networksquantum detector tomography2023-07-0110.1016/j.ifacol.2023.10.08885184823492