JAX-based Genetic Algorithm for Efficient Training of Variational Quantum Classifiers
Year: 2026
Type: conference-paper
Abstract: Genetic algorithms (GAs) have recently emerged as a competitive alternative to gradient-based optimization for training variational quantum classifiers (VQCs). However, existing GA-based training frameworks suffer from severe computational bottlenecks as a result of the repeated executions of large populations of quantum circuits across multiple data samples. In this work, we introduce a JAX-based genetic training framework for VQCs that takes advantage of population-level and data-level vectorization to drastically reduce execution time on classical simulation backends. Through an extensive experimental evaluation involving VQCs up to 15 qubits, multiple dataset sizes, and varying population sizes, we demonstrate speedups up to four orders of magnitude with respect to the state-of-the-art GA-based training of VQCs. Acceleration gains are particularly pronounced on GPUs. Importantly, these computational gains do not come at the expense of model performance, as comparable classification accuracy is achieved across all experiments performed.
Authors: Giovanni Acampora, Roberto Schiattarella
Institution: University of Naples Federico II
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7222977235