Quantum Machine Learning for Audio Classification with Applications to Healthcare
Michael Esposito, Glen Uehara, Andreas Spanias
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.