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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions
Creator	Bhattacharyya, Siddhartha Kairon, Pranav
source	ArXiv
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.
has issue date	2020-08-08(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f292d283e3ac28a10e53cbc3459544adc96ed819
resource representing a document's title	Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions
resource representing a document's body	covid:f292d283e3ac28a10e53cbc3459544adc96ed819#body_text
is schema:about of	named entity 'models' named entity 'numerous' named entity 'RELEVANT' named entity 'VARIEGATED' named entity 'APPLIED' »more»

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