论文标题
通过量子机学习的图像分类
Image Classification via Quantum Machine Learning
论文作者
论文摘要
在短时间内，量子计算，尤其是Quantum机器学习，通过世界各地的研究小组引起了很多兴趣。这可以在越来越多的模型模型中看到，该模型在一定程度上应用了量子原理。鄙视越来越多的模型的数量，在实际数据集上测试这些模型，而不仅仅是在合成数据集上测试这些模型。这项工作的目的是使用量子分类器将模式与二进制属性进行分类。特别是，我们显示了应用于图像数据集的完整量子分类器的结果。实验在处理平衡的分类问题以及少数群体最相关的不平衡课程时表现出优惠的产出。这在医疗领域是有希望的，通常重要的班级也是少数群体。
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.
