Quantum Machine Learning Applied to the Classification of Diabetes

TL;DR

This paper explores hybrid quantum machine learning techniques, specifically quantum classifiers combined with classical dimensionality reduction methods, to improve diabetes classification despite current hardware limitations.

Contribution

It introduces a hybrid approach using LDA and PCA with QSVC and VQC for diabetes classification, highlighting potential in quantum machine learning applications.

Findings

Encouraging results in quantum coding improvements

Effective use of LDA and PCA in quantum classifiers

Potential for future quantum industry applications

Abstract

Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold promise for future industries. As a weakness, quantum computing does not have enough qubits to justify its potential. This topic of study gives us encouraging results in the improvement of quantum coding, being the data preprocessing an important point in this research we employ two dimensionality reduction techniques LDA and PCA applying them in a hybrid way Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) in the classification of Diabetes.