Authors: Michael Siomau
Publish Date: 2014/01/09
Volume: 13, Issue: 5, Pages: 1211-1221
Abstract
The idea of information encoding on quantum bearers and its quantummechanical 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 is superior to 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
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