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arXiv · 2205.15588

QuanEstimation: An open-source toolkit for quantum parameter estimation

Mao Zhang·Huai-Ming Yu·Haidong Yuan·Xiaoguang Wang·Rafał Demkowicz-Dobrzański·Jing Liu
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Abstract

Quantum parameter estimation promises a high-precision measurement in theory, however, how to design the optimal scheme in a specific scenario, especially under a practical condition, is still a serious problem that needs to be solved case by case due to the existence of multiple mathematical bounds and optimization methods. Depending on the scenario considered, different bounds may be more or less suitable, both in terms of computational complexity and the tightness of the bound itself. At the same time, the metrological schemes provided by different optimization methods need to be tested against realization complexity, robustness, etc. Hence, a comprehensive toolkit containing various bounds and optimization methods is essential for the scheme design in quantum metrology. To fill this vacancy, here we present a Python-Julia-based open-source toolkit for quantum parameter estimation, which includes many well-used mathematical bounds and optimization methods. Utilizing this toolkit, all procedures in the scheme design, such as the optimizations of the probe state, control and measurement, can be readily and efficiently performed.

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Mao Zhang, Huai-Ming Yu, Haidong Yuan, Xiaoguang Wang, Rafał Demkowicz-Dobrzański, Jing Liu. 2022-10-25. QuanEstimation: An open-source toolkit for quantum parameter estimation. https://doi.org/10.1103/physrevresearch.4.043057

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