  In this paper we present a supervised machine learning quantum classifier. It
consists of a quantum data re-uploading classifier with binary trainable
parameters, the optimal values of which are found by a quantum search
algorithm. We show that we can reach the quadratic speed-up in optimization
trainable parameters compared to classical brute force search.
