COVID-19 outbreak prediction using quantum neural networks

Title

COVID-19 outbreak prediction using quantum neural networks

Creator

Kairon P.; Bhattacharyya S.

Description

Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Source

Advances in Intelligent Systems and Computing, Vol-1279, pp. 113-123.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Corona virus; COVID-19; Quantum machine learning; Quantum neural network

Coverage

Kairon P., Delhi Technological University, Bawana, Delhi, India; Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 21945357; ISBN: 978-981159289-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-15-9290-4_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098271346&doi=10.1007%2f978-981-15-9290-4_12&partnerID=40&md5=ce0e7de3dad5eb0260c5521637a6f30b