Analyzing SARS CoV-2 Patient Data Using Quantum Supervised Machine Learning

Abstract

The novel coronavirus disease 2019 (COVID-19) has created a serious threat to global health. We developed a new quantum machine learning (QML) assisted diagnostic method that can provide an accurate diagnosis to aid decision processes of medical providers. One of the key elements in our method was to implement the quantum variational method to efficiently classify data, taking crucial multiple correlations among the features into account. We established and fine-tuned this quantum classifier by using a group of data drawn from publicly available COVID-19 cases. We have shown that QML is capable of processing patient information efficiently and accurately for the diagnosis of COVID-19.