fast-EVOVAQ’s documentation

fast-EVOlutionary algorithms-based toolbox for VAriational Quantum circuits (EVOVAQ) is a novel evolutionary framework designedmto 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

Installation

You can install f-EVOVAQ via pip:

pip install fevovaq

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}}

Indices