QuanEstimation
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
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
- Install QuanEstimation via PyPI:
pip install quanestimation
- 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
Release files for quanestimation 0.5.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| quanestimation-0.5.0.tar.gz | 6.0 MB | Without provenance | Details for quanestimation-0.5.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| quanestimation-0.5.0-py3-none-any.whl | Python 3 | none | any | Details for quanestimation-0.5.0-py3-none-any.whl |
Total release size: 6.4 MB
Release files / quanestimation-0.5.0.tar.gz
| Download URL | quanestimation-0.5.0.tar.gz |
|---|---|
| Size | 6.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.3
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Release files / quanestimation-0.5.0-py3-none-any.whl
| Download URL | quanestimation-0.5.0-py3-none-any.whl |
|---|---|
| Size | 455.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.3
|
Security for quanestimation 0.5.0
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