  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.
