Divide et impera: hybrid multinomial classifiers from quantum binary models
Quantum Physics
2026-04-10 v1
Abstract
We investigate how to combine a collection of quantum binary models into a multinomial classifier. We employ a hybrid approach, adopting strategies like one-vs-one, one-vs-rest and a binary decision tree. We benchmark each method, by emphasizing their computational overhead and their impact on the quantum advantage. By comparison against a classical binary model (generalized using the same approach), we show that the decision tree represents a cost-effective solution, achieving similar accuracies to other methods with an overhead at most logarithmic in the total number of classes.
Cite
@article{arxiv.2604.08094,
title = {Divide et impera: hybrid multinomial classifiers from quantum binary models},
author = {Simone Roncallo and Angela Rosy Morgillo and Seth Lloyd and Chiara Macchiavello and Lorenzo Maccone},
journal= {arXiv preprint arXiv:2604.08094},
year = {2026}
}
Comments
5 pages, 1 figure;