  The idea of information encoding on quantum bearers and its
quantum-mechanical processing has revolutionized our world and brought mankind
on the verge of enigmatic era of quantum technologies. Inspired by this idea,
in present paper we search for advantages of quantum information processing in
the field of machine learning. Exploiting only basic properties of the Hilbert
space, superposition principle of quantum mechanics and quantum measurements,
we construct a quantum analog for Rosenblatt's perceptron, which is the
simplest learning machine. We demonstrate that the quantum perceptron superiors
its classical counterpart in learning capabilities. In particular, we show that
the quantum perceptron is able to learn an arbitrary (Boolean) logical
function, perform the classification on previously unseen classes and even
recognize the superpositions of learned classes -- the task of high importance
in applied medical engineering.
