This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer.

翻译：本文介绍了一个用于密度估计和监督分类的混合古典-分子程序。 程序作为量子电路在高度量子计算机模拟器中实施。 我们显示, 拟议的量子协议允许以有监督的学习方式估计概率密度函数和作出预测。 这个模型可以被普遍化, 以在高度量子计算机中找到密度矩阵的预期值。 演示了各种数据集的实验。 结果显示, 拟议的方法是一种可行的战略, 在高度量子计算机中实施有监督的分类和密度估计。