Metadata-Version: 2.5
Name: qubo-solver
Version: 0.9.0
Summary: A Quadratic Unconstrained Binary Optimization (QUBO) solver library using quantum and classical approaches.
Project-URL: Documentation, https://pasqal-io.github.io/qubo-solver/latest/
Project-URL: Issues, https://github.com/pasqal-io/qubo-solver/issues
Project-URL: Source, https://github.com/pasqal-io/qubo-solver
Author: Manu Lahariya, Charles Moussa
Author-email: Antoine Bussy <antoine.bussy@pasqal.com>, Monique Garzillo <monique.garzillo@pasqal.com>, Quentin Ma <quentin.ma@pasqal.com>, Yassine Naghmouchi <yassine.naghmouchi@pasqal.com>, Wesley da Silva Coelho <wesley.coelho@pasqal.com>, Clément de Terrasson de Montleau <clement.de-terrasson@pasqal.com>
License: MIT-derived
License-File: LICENSE.md
Keywords: combinatorial,optimization,quantum,qubo,solver
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: <3.15,>=3.10
Requires-Dist: emu-mps
Requires-Dist: emu-sv
Requires-Dist: numpy>=2
Requires-Dist: pandas
Requires-Dist: pasqal-cloud
Requires-Dist: pulser[torch]>=1.9.0
Requires-Dist: qoolqit[extras]>=1.4.0
Requires-Dist: scikit-learn
Requires-Dist: scikit-optimize
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: shapely
Requires-Dist: swiglpk>=5.0.13
Requires-Dist: torch
Provides-Extra: extras
Requires-Dist: cplex; extra == 'extras'
Description-Content-Type: text/markdown

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./docs/extras/assets/logo/qubosolver_logo_white.svg" width="60%">
    <source media="(prefers-color-scheme: light)" srcset="./docs/extras/assets/logo/qubosolver_logo_darkgreen.svg" width="60%">
    <img alt="Qubo Solver logo" src="./docs/extras/assets/logo/qubosolver_logo_darkgreen.svg" width="60%">
  </picture>
</p>

<p align="center">
  <strong>Qubo Solver</strong> is a Python library for solving Quadratic Unconstrained Binary Optimization (QUBO) problems on Pasqal's neutral-atom quantum processors.
</p>

> 🔥 Can't wait to try it? Jump straight to the [30-Second Quickstart](https://pasqal-io.github.io/qubo-solver/latest/get_started/quickstart/).
>
> 🆘 Need help? Check out the [Help](https://pasqal-io.github.io/qubo-solver/latest/help/faq/) section for FAQ, troubleshooting, and contact info.

**For more detailed information, [check out the documentation](https://pasqal-io.github.io/qubo-solver/latest/)**.

## Install from PyPI
Qubo Solver can be installed from PyPI with your favorite pyproject-compatible Python manager.
On `pip`, for example:

```sh
pip install qubo-solver
```

## Add Qubo Solver as a dependency
For usage within a project with a corresponding `pyproject.toml` file, you can add
`qubo-solver` to the list of dependencies as follows:

```toml
[project]
dependencies = [
  "qubo-solver"
]
```

## Install from source
If you wish to install directly from the source, for example, if you are developing code for Qubo Solver, you can:

1) Clone the [Qubo Solver GitHub repository](https://github.com/pasqal-io/qubo-solver)

  ```sh
  git clone https://github.com/pasqal-io/qubo-solver.git
  ```

2) Setup an environment for developing. From your `qubo-solver` folder, again install with your favorite environment/package managers.
  On `venv`/`pip`, for example:

  ```sh
  python -m venv .venv
  source .venv/bin/activate
  pip install --group dev -e .
  ```


# Getting in touch

- [Pasqal Community Portal](https://community.pasqal.com/) (to learn about Quantum Computing on Pasqal's platform).
- [GitHub Repository](https://github.com/pasqal-io/qubo-solver/) (source code, issue tracker).
- [Professional Support](https://www.pasqal.com/contact-us/) (e.g., technical support, custom licensing, a variant of this library optimized for your workload, purchasing your own QPU or remote access to one, etc.).
