A Quantum Model for Autonomous Learning Automata

TL;DR

This paper introduces a quantum analog of the perceptron, demonstrating its superior learning capabilities over classical models, including learning arbitrary logical functions and recognizing superpositions, with potential applications in medical engineering.

Contribution

The paper presents the first quantum perceptron model that leverages basic quantum properties to outperform classical perceptrons in learning tasks.

Findings

Quantum perceptron can learn arbitrary Boolean functions

It can classify unseen classes and recognize superpositions

Quantum model shows improved learning capabilities

Abstract

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.