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              <text>COVID-19 outbreak prediction using quantum neural networks</text>
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              <text>Corona virus; COVID-19; Quantum machine learning; Quantum neural network</text>
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              <text>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.</text>
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              <text>Kairon P.; Bhattacharyya S.</text>
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              <text>Advances in Intelligent Systems and Computing, Vol-1279, pp. 113-123.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-15-9290-4_12" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-9290-4_12&lt;/a&gt;
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              <text>ISSN: 21945357; ISBN: 978-981159289-8</text>
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              <text>Kairon P., Delhi Technological University, Bawana, Delhi, India; Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, India</text>
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