EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational Quantum circuits Acampora, Giovanni Cano Gutiérrez, Carlos Chiatto, Angela Soto Hidalgo, José Manuel Vitiello, Autilia Evolutionary algorithms Python package Quantum computing Evolutionary Algorithms (EAs) are becoming increasingly popular for training Variational Quantum Circuits (VQCs) due to their ability to conserve quantum resources. However, there is currently a lack of user-friendly tools for implementing this approach. To address this issue, this paper proposes EVOVAQ, a Python-based framework designed to simplify the use of EAs for training VQCs. EVOVAQ seamlessly integrates evolutionary computation with quantum libraries such as Qiskit, making it easy to use for both quantum computing and EAs communities. Furthermore, EVOVAQ’s scalability enables the development of customized solutions, promoting innovation in the quantum computing field. 2024-07-29T10:17:06Z 2024-07-29T10:17:06Z 2024-05-09 journal article Acampora, Giovanni, et al. EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational Quantum circuits. SoftwareX 26 (2024) 101756 [10.1016/j.softx.2024.101756] https://hdl.handle.net/10481/93556 10.1016/j.softx.2024.101756 eng http://creativecommons.org/licenses/by-nc/4.0/ open access Atribución-NoComercial 4.0 Internacional Elsevier