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  • Efficient identification of unitary quantum processes

    Author(s)
    Wang, Y
    Yin, Q
    Dong, D
    Qi, B
    Petersen, IR
    Hou, Z
    Yonezawa, H
    Xiang, GY
    Griffith University Author(s)
    Wang, Yuanlong
    Year published
    2017
    Metadata
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    Abstract
    Identifying an unknown unitary quantum process is a fundamental task in the development of quantum technology. In this paper, we propose an efficient identification algorithm for estimating unitary processes. In the method, input pure states are used and a fast quantum state tomography algorithm is developed to reconstruct the output states. Then the information of the output states is used to estimate the unitary process. The identification algorithm has computational complexity O(d 3 ) for a d-dimensional system. Numerical results show that the proposed identification algorithm is much more efficient than the maximum ...
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    Identifying an unknown unitary quantum process is a fundamental task in the development of quantum technology. In this paper, we propose an efficient identification algorithm for estimating unitary processes. In the method, input pure states are used and a fast quantum state tomography algorithm is developed to reconstruct the output states. Then the information of the output states is used to estimate the unitary process. The identification algorithm has computational complexity O(d 3 ) for a d-dimensional system. Numerical results show that the proposed identification algorithm is much more efficient than the maximum likelihood estimation method and works well for input mixed states with high purity. An analytical upper bound for the identification error is also provided, and numerical simulations and experimental results on quantum optical systems verify the theoretical results.
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    Conference Title
    2017 Australian and New Zealand Control Conference, ANZCC 2017
    DOI
    https://doi.org/10.1109/ANZCC.2017.8298511
    Subject
    Quantum physics
    Publication URI
    http://hdl.handle.net/10072/402400
    Collection
    • Conference outputs

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