Quantum correlation alignment for unsupervised domain adaptation
This work provides a quantum computing approach to domain adaptation, potentially offering speedups for machine learning tasks, but it is incremental as it adapts an existing classical method to quantum hardware.
The authors tackled the problem of domain shift in unsupervised domain adaptation by implementing the classical correlation alignment (CORAL) algorithm on quantum devices, achieving competitive performance with classical methods in numerical experiments on synthetic and handwritten digit datasets.
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.