Daniel Sierra-Sosa1,*, Juan D. Arcila-Moreno2, Begonya Garcia-Zapirain3, Adel Elmaghraby1
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1849-1861, 2021, DOI:10.32604/cmc.2021.013196
Abstract 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 More >
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