Petersen, IanMa, HailanDong, DaoyiEgerstedt, Magnus2024-01-31December 19781665436601http://hdl.handle.net/1885/312474Quantum 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.This work was supported by the Australian Research Council under Grants DP180101805 and DP190103615.application/pdfen-AU© 2021 IEEE202110.1109/CDC45484.2021.96833152022-10-02