@inproceedings{403d0c92aa504fd4b6c40a532a7b8c2c,
title = "Quantum Machine Learning for Audio Classification with Applications to Healthcare",
abstract = "Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented.",
keywords = "COVID-19, cough audio, quantum computing, quantum machine learning, quantum noise, spectral features",
author = "Michael Esposito and Glen Uehara and Andreas Spanias",
note = "Funding Information: Portions of this study have been supported by the SenSIP center and the REU award 1659871. Additional support was obtained from the SenSIP NSF I/UCRC project Award 1540040. Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 ; Conference date: 18-07-2022 Through 20-07-2022",
year = "2022",
doi = "10.1109/IISA56318.2022.9904377",
language = "English (US)",
series = "13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022",
}