We report the effect of comorbidity on the survival rates of COVID-19 using quantum machine learning. Recent understanding of the novel Coronavirus aims to verify the target organ of the virus, which could lead shortly to significant advances in the diagnosis and treatment of infected patients. An overview of the impact of the SARS-CoV-2 virus based on many different parameters such as age, type of comorbidity and gender have been studied. The data calculations are done manually by referring to parent articles and using machine learning and quantum machine learning algorithm. It is helpful to verify the target age group, gender at risk of infection, and survival rates of the person. The data used is classical data and quantum algorithms were run on it. We found out that the accuracy has increased to the classical machine learning state vector machine results. We found that pulmonary diseases are the most harmful type of comorbidity when an individual gets infected with COVID-19.