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  • Two-Stage Estimation for Quantum Detector Tomography: Error Analysis, Numerical and Experimental Results

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    Wang475157-Accepted.pdf (390.9Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Wang, Yuanlong
    Yokoyama, Shota
    Dong, Daoyi
    Petersen, Ian R
    Huntington, Elanor H
    Yonezawa, Hidehiro
    Griffith University Author(s)
    Wang, Yuanlong
    Year published
    2021
    Metadata
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    Abstract
    Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity O(nd2M) , where n is the number of d -dimensional detector matrices and M is ...
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    Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity O(nd2M) , where n is the number of d -dimensional detector matrices and M is the number of different probe states. An error upper bound is established, and optimization on the coherent probe states is investigated. We perform simulation and a quantum optical experiment to testify the effectiveness of the TSE method.
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    Journal Title
    IEEE Transactions on Information Theory
    Volume
    67
    Issue
    4
    DOI
    https://doi.org/10.1109/tit.2021.3062596
    Copyright Statement
    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Communications Technologies
    Publication URI
    http://hdl.handle.net/10072/403361
    Collection
    • Journal articles

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