  Despite the great efforts to find an effective way for COVID-19 prediction,
the virus nature and mutation represent a critical challenge to diagnose the
covered cases. However, developing a model to predict COVID-19 via Chest X-Ray
(CXR) images with accurate performance is necessary to help in early diagnosis.
In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN)
model used the random quantum circuits (RQCs) as a base to detect COVID-19
patients with CXR images. A collection of 6952 CXR images, including 1161
COVID-19, 1575 normal, and 5216 pneumonia images, were used as a dataset in
this work. The proposed HQCNN model achieved higher performance with an
accuracy of 98.4\% and a sensitivity of 99.3\% on the first dataset cases.
Besides, it obtained an accuracy of 99\% and a sensitivity of 99.7\% on the
second dataset cases. Also, it achieved accuracy, and sensitivity of 88.6\%,
and 88.7\%, respectively, on the third multi-class dataset cases. Furthermore,
the HQCNN model outperforms various models in balanced accuracy, precision,
F1-measure, and AUC-ROC score. The experimental results are achieved by the
proposed model prove its ability in predicting positive COVID-19 cases.
