This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning systems (classical + quantum) can reasonably bring benefits, not only in terms of compu- tational acceleration but in understanding the underlying phenomena and mechanisms; that will lead to the creation of new forms of machine learning, as well as to a strong development in the world of quantum computation. The chosen dataset is based on a 2D binary classification generator, which helps test the effectiveness of specific algorithms; it is a set of 2D points forming two interspersed semicircles. It displays two disjointed data sets in a two-dimensional representation space: the features are, therefore, the individual points’ two coordinates, x1 and x2 . The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognis- ing and classifying points.

An Example of Use of Variational Methods in Quantum Machine Learning

Marco Simonetti
;
Damiano Perri;Osvaldo Gervasi
2022-01-01

Abstract

This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning systems (classical + quantum) can reasonably bring benefits, not only in terms of compu- tational acceleration but in understanding the underlying phenomena and mechanisms; that will lead to the creation of new forms of machine learning, as well as to a strong development in the world of quantum computation. The chosen dataset is based on a 2D binary classification generator, which helps test the effectiveness of specific algorithms; it is a set of 2D points forming two interspersed semicircles. It displays two disjointed data sets in a two-dimensional representation space: the features are, therefore, the individual points’ two coordinates, x1 and x2 . The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognis- ing and classifying points.
978-3-031-10522-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1536073
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