This undergraduate thesis consists in classifying images obtained from chest computed tomography (CT) scans from patients who have had COVID-19 before by using Neural Networks. The Q-NAS model is a quantum inspired algorithm to search for deep networks by assembling substructures. The basic premise of a NAS model (Neural Architecture Search) is the capability of automatically generating and searching the best neural network architectures, without requiring advanced machine learning knowledge from the user. The Q-NAS has the same premise but using quantum physics paradigms which improves the accuracy and the convergence time. Because of these advantages, the Q-NAS model was applied to the CT images and classified them in six different classes according to the post-covid lung pattern found. The purpose of this undergraduate project is to generate new neural networks capable of classifying the post-covid patterns with a new database and test those models by using new inputs that were obtained from Pedro Ernesto University Hospital s patients.