Metadata-Version: 2.4
Name: fevovaq
Version: 1.0.2
Summary: fast-EVOlutionary algorithms toolbox for VAriational Quantum circuits
Home-page: https://github.com/Quasar-UniNA/EVOVAQ
Author: Angela Chiatto
Author-email: angela.chiatto@unina.it
License: MIT
Keywords: Quantum Computing,Evolutionary Algorithms,Variational Quantum Circuits,Variational Quantum Algorithms
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.23.5
Requires-Dist: tabulate==0.8.10
Requires-Dist: tqdm==4.64.1
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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Dynamic: keywords
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# fast-EVOVAQ [![Made at Quasar!](https://img.shields.io/badge/Unina-%20QuasarLab-blue)](http://quasar.unina.it) [![Made at Quasar!](https://img.shields.io/badge/Documentation-%20Readthedocs-brightgreen)](https://f-evovaq.readthedocs.io/en/latest/index.html)

**fast-EVOlutionary algorithms-based toolbox for VAriational Quantum circuits (f-EVOVAQ)** is a novel evolutionary framework designed
to easily train variational quantum circuits through evolutionary techniques on GPUs, and to have a simple interface between
these algorithms and quantum libraries, such as Qiskit and Pennylane.

**Optimizers in f-EVOVAQ:**

* Genetic Algorithm

* Differential Evolution

* Memetic Algorithm

* Big Bang Big Crunch

* Particle Swarm Optimization

* CHC Algorithm

* Hill Climbing (to be integrated in Memetic Algorithms)

## Installation

You can install f-EVOVAQ via ``pip``:

```bash
pip install f-evovaq
```

Pip will handle all dependencies automatically and you will always install the latest version.

## Credits
If you use f-EVOVAQ in your work, please cite the following paper:

### BibTeX Citation

```bibtex
@article{f-evovaq,
  title={f-EVOVAQ: A GPU-based Framework for Evolutionary Training of Variational Quantum Algorithms},
  author={Acampora, Giovanni and Chiatto, Angela and Vitiello, Autilia},
  journal={Accepted to 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
  year={2026},
  publisher={IEEE}}

