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.

中文翻译：

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通过量子机器学习进行图像分类

量子计算，尤其是量子机器学习，在很短的时间内，通过世界各地的研究小组获得了很多兴趣。这可以从越来越多的提出的模式分类模型中看出，在一定程度上应用了量子原理。尽管模型数量不断增加，但在真实数据集上而不是仅在合成数据集上测试这些模型是无效的。这项工作的目标是使用量子分类器对具有二元属性的模式进行分类。特别地，我们展示了应用于图像数据集的完整量子分类器的结果。实验在处理平衡分类问题以及少数类最相关的不平衡类时显示出有利的输出。这在医疗领域很有前景，