  Convolutional Neural Networks (CNN) are used mainly to treat problems with
many images characteristic of Deep Learning. In this work, we propose a hybrid
image classification model to take advantage of quantum and classical
computing. The method will use the potential that convolutional networks have
shown in artificial intelligence by replacing classical filters with
variational quantum filters. Similarly, this work will compare with other
classification methods and the system's execution on different servers. The
algorithm's quantum feasibility is modelled and tested on Amazon Braket
Notebook instances and experimented on the Pennylane's philosophy and
framework.
