The correlation alignment algorithm (CORAL), a representative domain adaptation algorithm, decorrelates and aligns a labeled source domain dataset to an unlabeled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely, the synthetic data, the synthetic-Iris data, and the handwritten digit data, are presented to evaluate the performance of our paper. The simulation results prove that the variational quantum correlation alignment algorithm can achieve competitive performance compared with the classical CORAL.

中文翻译：

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用于无监督域自适应的量子相关对准

相关性对齐算法（CORAL）是一种代表性的域自适应算法，用于将标记的源域数据集与未标记的目标域数据集进行解相关和对齐，以最小化域偏移，从而可以将分类器应用于预测目标域标签。在本文中，我们通过两种不同的方法在量子设备上实现了CORAL。一种方法是利用量子基本线性代数子例程在给定数据样本的数量和维数上实现指数级加速的CORAL。另一种方法是通过变分混合量子经典方法实现的。此外，还对CORAL进行了三种不同类型的数据集的数值实验，即合成数据，合成虹膜数据和手写数字数据，以评估本文的性能。