# An example of use of Variational Methods in Quantum Machine Learning

arXiv: https://arxiv.org/abs/2208.04316 · HTML: https://mlsanity.com/paper/2208.04316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Towards Quantum Machine Learning with Tensor Networks](https://mlsanity.com/paper/1803.11537/) (2018)
- [Skin Cancer Classification using Inception Network and Transfer Learning](https://mlsanity.com/paper/2111.02402/) (2021)
- [Binary classification of proteins by a Machine Learning approach](https://mlsanity.com/paper/2111.01975/) (2021)
- [A new method for binary classification of proteins with Machine Learning](https://mlsanity.com/paper/2111.01976/) (2021)

## Built on by (most cited first, 1 of 1)

- [Shot-frugal and Robust quantum kernel classifiers](https://mlsanity.com/paper/2210.06971/) (2022)

## Abstract (arXiv)

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, x1 and x2. The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.