Quantum correlation alignment for unsupervised domain adaptation

Abstract

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.

11 pages, 9 figures

References in corpus (7)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum algorithm for solving linear systems of equations
• Quantum random access memory
• Quantum Data Fitting
• Correlation Alignment for Unsupervised Domain Adaptation
• Quantum locally linear embedding for nonlinear dimensionality reduction
• Quantum transfer component analysis for domain adaptation

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