A Hybrid Quantum-Classical Framework for Binary Classification in Online Learning

Corrado Loglisci, Ivan Diliso, Donato Malerba

SEBD 2023, 88-89

In this paper, we show how typical issues of online learning can be equally addressed with the properties of quantum mechanics, until to offer better results. We aim at keeping the classification capabilities, which have learned on previously processed data instances, preserved as much as possible, and then acquiring new knowledge on new data instances. To this end, we propose a class of quantum neural networks, variational quantum circuits, that being adapted over time by exploiting techniques of model update used in classical neural networks. Experiments are performed on real-world datasets with quantum simulators.