Multi-class classification based on quantum state discrimination

Giuntini, Roberto; Granda Arango, Andrés Camilo; Freytes, Hector; Holik, Federico Hernan; Sergioli, Giuseppe

doi:10.1016/j.fss.2023.03.012

Multi-class classification based on quantum state discrimination

Roberto Giuntini
(
- Cagliari U.
)
,
Andrés Camilo Granda Arango
(
- Cagliari U.
)
,
Hector Freytes
(
- Cagliari U.
)
,
Federico Hernan Holik
(
- Cagliari U. and
- La Plata U.
)
,
Giuseppe Sergioli
(
- Cagliari U.
)

Mar 28, 2023

15 pages

Published in:

Fuzzy Sets Syst. 467 (2023) 8509

Published: Mar 28, 2023

DOI:

10.1016/j.fss.2023.03.012 (publication)

reference search3 citations

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Abstract: (Elsevier B.V.)

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.

Quantum-inspired algorithms
Multi-class classification
Pretty Good Measurement

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