Image Classification via Quantum Machine Learning
This work addresses a gap in testing quantum classifiers on real data, which could benefit fields like medical imaging where minority classes are critical.
The authors tackled the problem of applying quantum machine learning to real image datasets, achieving favorable results in both balanced and imbalanced classification scenarios, with potential relevance for medical applications.
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.