论文标题
无监督域适应的量子相关对齐
Quantum correlation alignment for unsupervised domain adaptation
论文作者
论文摘要
相关比对（珊瑚），代表域的适应性（DA）算法，将标记的源域数据集与未标记的目标域数据集排列，并使标记的源域数据集对齐，以最大程度地减少域移位，以便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施珊瑚。一种方法利用量子基本线性代数子例程（QBLA）在给定数据样本的数字和维度中以指数加速实现珊瑚。另一种方法是通过杂交量子古典过程来实现的。此外，还提供了具有三种不同类型的数据集的珊瑚的数值实验，即合成数据，合成-IRIS数据，手写数字数据，以评估我们的工作的性能。模拟结果证明，与经典珊瑚相比，变分量子相关比对算法（VQCORAL）可以实现竞争性能。
Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled 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 (QBLAS) 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, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.
