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.
EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational Quantum circuits / Acampora, G.; Cano Gutierrez, C.; Chiatto, A.; Soto Hidalgo, J. M.; Vitiello, A.. - In: SOFTWAREX. - ISSN 2352-7110. - 26:(2024). [10.1016/j.softx.2024.101756]
EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational Quantum circuits
Acampora G.;Chiatto A.;Vitiello A.
2024
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
