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Despite the great efforts to find an effective way for coronavirus disease 2019 (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 images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional neural network (HQ-CNN) model using random quantum circuits as a base to detect COVID-19 patients with chest X-ray images is presented. A collection of 5445 chest X-ray images, including 1350 COVID-19, 1350 normal, 1345 viral pneumonia, and 1400 bacterial pneumonia images, were used to evaluate the HQ-CNN. The proposed HQ-CNN model has achieved higher performance with an accuracy of 98.6% and a recall of 99% on the first experiment (COVID-19 and normal cases). Besides, it obtained an accuracy of 98 …