QuanEstimation is a Python-Julia-based open-source toolkit for quantum parameter estimation, which can be used to perform general evaluations of many metrological tools and scheme designs in quantum parameter estimation.
The documentation of QuanEstimation can be found here.
- Install QuanEstimation via PyPI:
pip install quanestimation
- Download the package and install it in the terminal:
git clone https://github.com/QuanEstimation/QuanEstimation.git
cd QuanEstimation
pip install .
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If you use QuanEstimation in your research, please cite the following papers:
[1] M. Zhang, H.-M. Yu, H. Yuan, X. Wang, R. Demkowicz-Dobrzański, and J. Liu, QuanEstimation: An open-source toolkit for quantum parameter estimation, Phys. Rev. Res. 4, 043057 (2022).
[2] H.-M. Yu and J. Liu, QuanEstimation.jl: An open-source Julia framework for quantum parameter estimation, Fundam. Res. (2025).
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Development of the GRAPE algorithm in quantum parameter estimation can be found in the following papers:
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auto-GRAPE:
M. Zhang, H.-M. Yu, H. Yuan, X. Wang, R. Demkowicz-Dobrzański, and J. Liu, QuanEstimation: An open-source toolkit for quantum parameter estimation, Phys. Rev. Res. 4, 043057 (2022).
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GRAPE for single-parameter estimation:
J. Liu and H. Yuan, Quantum parameter estimation with optimal control, Phys. Rev. A 96, 012117 (2017).
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GRAPE for multiparameter estimation:
J. Liu and H. Yuan, Control-enhanced multiparameter quantum estimation, Phys. Rev. A 96, 042114 (2017).
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