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Quantum Machine Learning
Divide et impera: hybrid multinomial classifiers from quantum binary models
arXiv
Authors: Simone Roncallo, Angela Rosy Morgillo, Seth Lloyd, Chiara Macchiavello, Lorenzo Maccone
Year
2026
Paper ID
45396
Status
Preprint
Abstract Read
~2 min
Abstract Words
86
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We investigate how to combine a collection of quantum binary models into a multinomial classifier.
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