An example of use of Variational Methods in Quantum Machine Learning
- URL: http://arxiv.org/abs/2208.04316v1
- Date: Sun, 7 Aug 2022 03:52:42 GMT
- Title: An example of use of Variational Methods in Quantum Machine Learning
- Authors: Marco Simonetti and Damiano Perri and Osvaldo Gervasi
- Abstract summary: This paper introduces a quantum neural network for the binary classification of points of a specific geometric pattern on a plane.
The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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