  In the current era, quantum resources are extremely limited, and this makes
difficult the usage of quantum machine learning (QML) models. Concerning the
supervised tasks, a viable approach is the introduction of a quantum locality
technique, which allows the models to focus only on the neighborhood of the
considered element. A well-known locality technique is the k-nearest neighbors
(k-NN) algorithm, of which several quantum variants have been proposed.
Nevertheless, they have not been employed yet as a preliminary step of other
QML models, whereas the classical counterpart has already proven successful. In
this paper, we present (i) an implementation in Python of a QML pipeline for
local classification, and (ii) its extensive empirical evaluation.
Specifically, the quantum pipeline, developed using Qiskit, consists of a
quantum k-NN and a quantum binary classifier. The results have shown the
quantum pipeline's equivalence (in terms of accuracy) to its classical
counterpart in the ideal case, the validity of locality's application to the
QML realm, but also the strong sensitivity of the chosen quantum k-NN to
probability fluctuations and the better performance of classical baseline
methods like the random forest.
