Quantum-inspired minimum distance classification in a biomedical context

Sergioli G.

First

;RUSSO, GIORGIO;Santucci E.;STEFANO, ALESSANDRO;Torrisi S. E.;Palmucci S.;Vancheri C.;Giuntini R.

2018-01-01

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Abstract

We propose an application of a quantum-inspired version of the Nearest Mean Classifier (NMC) (G. Sergioli, E. Santucci, L. Didaci, J. A. Miszczak and R. Giuntini, A quantum inspired version of the NMC classifier, Soft Comput.22(3) (2018) 691. G. Sergioli, G. M. Bosyk, E. Santucci and R. Giuntini, A quantum-inspired version of the classification problem, Int. J. Theo. Phys.56(12) (2017) 3880. E. Santucci and G. Sergioli, Classification problem in a quantum framework, in quantum foundations, probability and information, Proc. Quantum and Beyond Conf., 13–16 June 2016, Vaxjo, Sweden, A. Khrennikov and T. Bourama, Springer-Berlin, Germany, 2018 (in press, 2018). E. Santucci, Quantum minimum distance classifier, Entropy19(12) (2017) 659.) to a biomedical context. In particular, we benchmark the performances of such a quantum-variant of NMC against NMC and other (nonlinear) classifiers with respect to the problem of classifying the probability of survival for patients affected by idiopathic pulmonary fibrosis (IPF).