Scalable quantum tomography with fidelity estimation
Author(s)
Wang, Jun; Han, Zhao-Yu; Wang, Song-Bo; Li, Zeyang; Mu, Liang-Zhu; Fan, Heng; Wang, Lei; ... Show more Show less
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We propose a quantum tomography scheme for pure qudit systems which adopts a certain version of random basis measurements and a generative learning method, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states. We prove the validity of the scheme theoretically, and we perform numerically simulated experiments on several target states that have compact matrix product state representation, demonstrating its efficiency and robustness. We find the number of replicas required by a fixed fidelity criterion grows only linearly as the system size scales up, which saturates a lower bound from information theory. Thus the scheme achieves the highest possible scalability that is crucial for practical quantum state tomography. Keywords: Quantum tomography; Machine learning; Tensor network methods
Date issued
2020-03Department
Massachusetts Institute of Technology. Department of Physics; Massachusetts Institute of Technology. Research Laboratory of Electronics; MIT-Harvard Center for Ultracold AtomsJournal
Physical Review A
Publisher
American Physical Society (APS)
Citation
Wang, Jun, et al. "Scalable quantum tomography with fidelity estimation." Physical Review A, 101, 3 (March 2020): 032321. © 2020 American Physical Society
Version: Final published version
ISSN
2469-9926
2469-9934