A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms

Machine Learning (ML) is a predominant tool nowadays to solve several challenges in different industries, such as credit scoring Provenzano et al. [2020], fraud analysis Tiwari et al. [2021], product recommendation Rohde et al. [2018], and demand forecasting Masini et al. [2020], among other extensively explored use cases. Under this premise, the research of the quantum computing properties applied to ML has expanded rapidly in recent years since a proven advantage could be a highly useful cross-industry. The recent progress of these explorations in Quantum Machine Learning (QML) Mishra et al. [2021] put a spotlight on quantum technology, introducing a challenge to determine if QML will provide an advantage over classical machine learning or not. The actual devices are noisy, meaning that the depth or consecutive gate operations are limited [Ristè et al., 2013, Burnett et al., 2019, Wang et al., 2021]. Qubits will lose their entanglement and so, the information. These devices make up the NISQ era Preskill [2018] and limit the use of quantum algorithms or hybrid algorithms to be useful Callison and Chancellor [2022].