A Quantum Model for Autonomous Learning Automata

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.