Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification

TL;DR

This paper presents a Python-implemented quantum machine learning pipeline for local classification, empirically evaluating its accuracy and sensitivity, and comparing it to classical methods like random forests.

Contribution

It introduces a novel quantum local classification pipeline using quantum k-NN and binary classifiers, with extensive empirical evaluation demonstrating its effectiveness and limitations.

Findings

Quantum pipeline achieves accuracy comparable to classical methods in ideal conditions.

Quantum k-NN shows high sensitivity to probability fluctuations.

Classical methods like random forests outperform the quantum pipeline in some cases.

Abstract

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.