  Quantum Computing and especially Quantum Machine Learning, in a short period
of time, has gained a lot of interest through research groups around the world.
This can be seen in the increasing number of proposed models for pattern
classification applying quantum principles to a certain degree. Despise the
increasing volume of models, there is a void in testing these models on real
datasets and not only on synthetic ones. The objective of this work is to
classify patterns with binary attributes using a quantum classifier. Specially,
we show results of a complete quantum classifier applied to image datasets. The
experiments show favorable output while dealing with balanced classification
problems as well as with imbalanced classes where the minority class is the
most relevant. This is promising in medical areas, where usually the important
class is also the minority class.
