Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By including feature selection techniques and geometrical transformations, enhanced quantum state preparation is achieved. Also, a representation based on the Stokes parameters in the Poincaré Sphere is possible for visualizing the data. Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients. We used the implemented version of VQC available on IBM’s framework Qiskit, and obtained with two and three qubits an accuracy of 70% and 72% respectively.

Quantum machine learning; data preprocessing; stokes parameters; Poincare sphere