f-EVOVAQ: A GPU-based Framework for Evolutionary Training of Variational Quantum Algorithms
Year: 2026
Type: conference-paper
Abstract: In the field of quantum computing, Variational Quantum Algorithms (VQAs) combine quantum circuits with classical optimization to perform several tasks including classification and regression. Although gradient-based methods are commonly used for their training, these methods suffer from barren plateaus, noise sensitivity, and high computational costs. In this scenario, Evolutionary Algorithms (EAs) provide a gradient-free alternative for training VQAs enabling the discovery of high-quality solutions while requiring limited hardware resources. Unfortunately, the number of software tools aimed to the application of EAs for training of VQAs is limited and, moreover, they rarely exploits GPU acceleration. In order to bridge this gap, this work presents f-EVOVAQ, a Python package that extends the EVOVAQ framework by implementing vectorized evolutionary operators on GPU using CuPy arrays. The package enables efficient population-level optimization while remaining user-friendly for non-experts in evolutionary computation. Experiments on quantum classification tasks demonstrate significant reductions in optimization time and improved scalability, with limitations arising from GPU–CPU data transfers.
Institution: University of Naples Federico II
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7172245401