An example of use of Variational Methods in Quantum Machine Learning

TL;DR

This paper explores a hybrid classical-quantum deep learning approach using variational methods for binary classification of a geometric pattern, aiming to enhance understanding and efficiency in quantum machine learning.

Contribution

It introduces a quantum neural network model optimized for minimal parameters to classify a two-moons dataset, demonstrating potential benefits of hybrid quantum-classical systems.

Findings

Quantum neural network successfully classifies two-moons dataset

Hybrid system shows potential for computational acceleration

Minimal parameter model achieves accurate classification

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 computational 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, $x_1$ and $x_2$. The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.