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QuanEstimation

GitHub release (latest by date) License: BSD-3-Clause CI codecov Downloads Tutorial DOI

QuanEstimation is an open-source Python–Julia toolkit for quantum parameter estimation. It supports general evaluations of metrological tools and the design of quantum estimation schemes.

Documentation

Docs Stable

The documentation of QuanEstimation can be found here. Docstrings generally follow the Google Python Style Guide; some older modules still use NumPy-style sections.

Installation

PyPI

  1. Install QuanEstimation via PyPI:
pip install quanestimation
  1. Clone the repository and install from the checkout:
git clone https://github.com/QuanEstimation/QuanEstimation.git
cd QuanEstimation
pip install .

The Python package requires Python >= 3.10 and Julia 1.11 or newer in the 1.x series, and pins the Julia core to QuanEstimation 0.5.0. The core is installed and loaded through juliacall on first use. For local development against a QuanEstimation.jl checkout, set QuanEstimation_DEV_PATH to that checkout before the Julia core is first loaded; the bridge still checks that the checkout reports version 0.5.0.

Code Style

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.

Citation

  • 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. 6, 2167-2174 (2026).

  • 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).

Metadata

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