Scalable quantum tomography with fidelity estimation
Name
PhysRevA.101.032321.pdf
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804.62 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Wang, Jun
Han, Zhao-Yu
Wang, Song-Bo
Li, Zeyang
Mu, Liang-Zhu
Fan, Heng
Wang, Lei
Date Issued
March 2020
Journal
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
Abstract
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
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Research Laboratory of Electronics
MIT-Harvard Center for Ultracold Atoms
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevA.101.032321
