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arXiv · 2604.08094

Divide et impera: hybrid multinomial classifiers from quantum binary models

Simone Roncallo·Angela Rosy Morgillo·Seth Lloyd·Chiara Macchiavello·Lorenzo Maccone
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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.

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Simone Roncallo, Angela Rosy Morgillo, Seth Lloyd, Chiara Macchiavello, Lorenzo Maccone. 2026-04-09. Divide et impera: hybrid multinomial classifiers from quantum binary models. https://arxiv.org/abs/2604.08094

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