Marco Leonti
Multi-class classification based on quantum state discrimination
Giuntini R.;Granda Arango A. C.;Freytes H.;Holik F. H.;Sergioli G.
2023-01-01
Abstract
We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.File | Size | Format | |
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FSS.pdf Solo gestori archivio
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Multi_class_classifier_for_FSS.pdf open access
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616.97 kB | Adobe PDF | View/Open |
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