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  • Adaptive Quantum Process Tomography via Linear Regression Estimation

    Author(s)
    Yu, Q
    Dong, D
    Wang, Y
    Petersen, IR
    Griffith University Author(s)
    Wang, Yuanlong
    Year published
    2020
    Metadata
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    Abstract
    This paper proposes a recursively adaptive tomography protocol to improve the precision of quantum process estimation for finite dimensional systems. The problem of quantum process tomography is firstly formulated as a parameter estimation problem which can then be solved by the linear regression estimation method. An adaptive algorithm is proposed for the selection of subsequent input states given the previous estimation results. Numerical results show that the proposed adaptive process tomography protocol can achieve an improved level of estimation performance.This paper proposes a recursively adaptive tomography protocol to improve the precision of quantum process estimation for finite dimensional systems. The problem of quantum process tomography is firstly formulated as a parameter estimation problem which can then be solved by the linear regression estimation method. An adaptive algorithm is proposed for the selection of subsequent input states given the previous estimation results. Numerical results show that the proposed adaptive process tomography protocol can achieve an improved level of estimation performance.
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    Conference Title
    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    DOI
    https://doi.org/10.1109/SMC42975.2020.9283060
    Subject
    Computation Theory and Mathematics
    Publication URI
    http://hdl.handle.net/10072/402398
    Collection
    • Conference outputs

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