  Machine Learning classification models learn the relation between input as
features and output as a class in order to predict the class for the new given
input. Quantum Mechanics (QM) has already shown its effectiveness in many
fields and researchers have proposed several interesting results which cannot
be obtained through classical theory. In recent years, researchers have been
trying to investigate whether the QM can help to improve the classical machine
learning algorithms. It is believed that the theory of QM may also inspire an
effective algorithm if it is implemented properly. From this inspiration, we
propose the quantum-inspired binary classifier.
