We propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm 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.

我们提出了一种新技术，通过使用量子支持向量机自动生成用于分类的最优 ad-hoc anstze。这种有效的方法基于非排序遗传算法 II 多目标遗传算法，它允许最大化准确性和最小化 ansatz 大小。通过具有非线性数据集的实际示例证明了该技术的有效性，解释了结果电路及其输出。我们还展示了该技术的其他应用领域，这些领域加强了该方法的有效性，并与经典分类器进行了比较，以了解使用量子机器学习的优势。