QuanEstimation is a Python-Julia-based open-source toolkit for quantum parameter estimation. It 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. This project uses the Google Python Style Guide for docstrings.

1. Install QuanEstimation via PyPI:

pip install quanestimation

2. Download the package and install it in the terminal:

git clone https://github.com/QuanEstimation/QuanEstimation.git

cd QuanEstimation

pip install .

This project follows the PEP 8 Python code style guide. It is recommended to use tools such as black and flake8 for code formatting and linting.

• If you use QuanEstimation in your research, please cite:

  [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).

• Development of the GRAPE algorithm:

  • 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).

  • GRAPE for single-parameter estimation:

    J. Liu and H. Yuan, Quantum parameter estimation with optimal control,
    Phys. Rev. A 96, 012117 (2017).

  • GRAPE for multiparameter estimation:

    J. Liu and H. Yuan, Control-enhanced multiparameter quantum estimation,
    Phys. Rev. A 96, 042114 (2017).