fevovaq 1.0.2

fast-EVOlutionary algorithms toolbox for VAriational Quantum circuits

fast-EVOVAQ

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:

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

@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}}