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Quantum Machine Learning

Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions

arXiv

Authors: Pranav Kairon, Siddhartha Bhattacharyya

Year

2020

Paper ID

21607

Status

Preprint

Abstract Read

~2 min

Abstract Words

110

Citations

N/A

Abstract

There are numerous models of quantum neural networks that have been applied to variegated problems such as image classification, pattern recognition etc.Quantum inspired algorithms have been relevant for quite awhile. More recently, in the NISQ era, hybrid quantum classical models have shown promising results. Multi-feature regression is common problem in classical machine learning. Hence we present a comparative analysis of continuous variable quantum neural networks (Variational circuits) and quantum backpropagating multi layer perceptron (QBMLP). We have chosen the contemporary problem of predicting rise in COVID-19 cases in India and USA. We provide a statistical comparison between two models , both of which perform better than the classical artificial neural networks.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2020 reference point for readers tracking recent quantum research.
There are numerous models of quantum neural networks that have been applied to variegated problems such as image classification, pattern recognition etc.Quantum inspired...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2008.07617 [2] arXiv https://arxiv.org/abs/2008.07617 [3] Publisher https://arxiv.org/abs/2008.07617

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