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

TL;DR

This paper compares variational quantum circuits and quantum backpropagation neural networks for predicting COVID-19 case rises, demonstrating both outperform classical neural networks in a multi-feature regression task.

Contribution

It provides a comparative analysis of two quantum neural network models applied to COVID-19 outbreak prediction, highlighting their superior performance over classical models.

Findings

Both quantum models outperform classical neural networks.

Quantum models show promising results in NISQ-era applications.

The study offers statistical validation of quantum models' effectiveness.

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.