  The aim of this paper is to provide a quantum counterpart of the well known
minimum-distance classifier named Nearest Mean Classifier (NMC). In particular,
we refer to the following previous works: i) in Sergioli et al. 2016, we have
introduced a detailed quantum version of the NMC, named Quantum Nearest Mean
Classifier (QNMC), for two-dimensional problems and we have proposed a
generalization to abitrary dimensions; ii) in Sergioli et al. 2017, the
n-dimensional problem was analyzed in detail and a particular encoding for
arbitrary n-feature vectors into density operators has been presented. In this
paper, we introduce a new promizing encoding of arbitrary n-dimensional
patterns into density operators, starting from the two-feature encoding
provided in the first work. Further, unlike the NMC, the QNMC shows to be not
invariant by rescaling the features of each pattern. This property allows us to
introduce a free parameter whose variation provides, in some case, an
improvement of the QNMC performance. We show experimental results where: i) the
NMC and QNMC performances are compared on different datasets; ii) the effects
of the non-invariance under uniform rescaling for the QNMC are investigated.
