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:
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Genetic Algorithm
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Differential Evolution
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Memetic Algorithm
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Big Bang Big Crunch
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Particle Swarm Optimization
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CHC Algorithm
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Hill Climbing (to be integrated in Memetic Algorithms)
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}}
1 maintainer
Metadata
1 maintainer
Release files for fevovaq 1.0.3
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