Quantum Machine Learning Applied to the Classification of Diabetes
This addresses diabetes classification for medical applications, but is incremental as it applies existing quantum methods to a specific dataset.
The paper tackled diabetes classification using hybrid quantum machine learning methods, achieving encouraging results with dimensionality reduction techniques LDA and PCA applied to Quantum Support Vector Classifier and Variational Quantum Classifier.
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.