We propose a new technique for the automatic generation of optimal ad-hoc ans\"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

翻译：我们建议采用量子支持矢量机(QSVM)自动生成最佳的 ad-hoc ans\"atze 卫星进行分类的新技术。 这种高效方法基于NSGA-II的多目标遗传算法,它既能最大限度地提高精确度,又能最大限度地减少肛门尺寸。 通过一个非线性数据集的实用实例,解释由此产生的电路及其输出,可以证明该技术的有效性。 我们还展示了该技术的其他应用领域,加强了该方法的有效性,并与古典分类法进行了比较,以便了解使用量子机学习的好处。