  We introduce the quantum implementation of a binary classifier based on
cosine similarity between data vectors. The proposed quantum algorithm
evaluates the classifier on a set of data vectors with time complexity that is
logarithmic in the product of the set cardinality and the dimension of the
vectors. It is based just on a suitable state preparation like the retrieval
from a QRAM, a SWAP test circuit (two Hadamard gates and one Fredkin gate), and
a measurement process on a single qubit. Furthermore we present a simple
implementation of the considered classifier on the IBM quantum processor
ibmq_16_melbourne. Finally we describe the combination of the classifier with
the quantum version of a K-nearest neighbors algorithm within a hybrid
quantum-classical structure.
