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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Simonetti, M., Perri, D., Gervasi, O. (2022). An Example of Use of Variational Methods in Quantum Machine Learning. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13382. Springer, Cham. https://doi.org/10.1007/978-3-031-10592-0_43
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