Quantum-inspired minimum distance classi¯cation in a biomedical context

We propose an application of a quantum-inspired version of the Nearest Mean Classi¯er (NMC) (G. Sergioli, E. Santucci, L. Didaci, J. A. Miszczak and R. Giuntini, A quantum inspired version of the NMC classi¯er, Soft Comput. 22(3) (2018) 691. G. Sergioli, G. M. Bosyk, E. Santucci and R. Giuntini, A quantum-inspired version of the classi¯cation problem, Int. J. Theo. Phys. 56(12) (2017) 3880. E. Santucci and G. Sergioli, Classi¯cation 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 classi¯er, Entropy 19(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) classi¯ers with respect to the problem of classifying the probability of survival for patients a®ected by idiopathic pulmonary ¯brosis (IPF).