quanestimation 0.2.8
A package for quantum parameter estimation.
License: BSD 3-Clause License
Requires Python: >=3.10
Keywords: quanestimation

# QuanEstimation

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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.

## Documentation

[![Docs](https://github.com/QuanEstimation/QuanEstimation/actions/workflows/gh-deploy.yml/badge.svg)](https://github.com/QuanEstimation/QuanEstimation/actions/workflows/gh-deploy.yml)
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The documentation of QuanEstimation can be found [here](https://quanestimation.github.io/QuanEstimation/). 
This project uses the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.


## Installation

![PyPI](https://img.shields.io/pypi/v/QuanEstimation)

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 .
~~~

## Code Style

This project follows the [PEP 8](https://peps.python.org/pep-0008/) 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).](https://doi.org/10.1103/PhysRevResearch.4.043057)

  [2] H.-M. Yu and J. Liu, QuanEstimation.jl: An open-source Julia framework for quantum parameter estimation,  
  [Fundam. Res. (2025).](https://doi.org/10.1016/j.fmre.2025.02.020)

* 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).](https://doi.org/10.1103/PhysRevResearch.4.043057)

  * **GRAPE for single-parameter estimation**:
  
    J. Liu and H. Yuan, Quantum parameter estimation with optimal control,  
    [Phys. Rev. A **96**, 012117 (2017).](https://doi.org/10.1103/PhysRevA.96.012117)

  * **GRAPE for multiparameter estimation**:
  
    J. Liu and H. Yuan, Control-enhanced multiparameter quantum estimation,  
    [Phys. Rev. A **96**, 042114 (2017).](https://doi.org/10.1103/PhysRevA.96.042114)