# Quantum correlation alignment for unsupervised domain adaptation

arXiv: https://arxiv.org/abs/2005.03355 · HTML: https://mlsanity.com/paper/2005.03355/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 3 of 3)

- [Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction](https://mlsanity.com/paper/2512.02610/) (2025)
- [Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages](https://mlsanity.com/paper/2411.01822/) (2024)
- [Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation](https://mlsanity.com/paper/2603.15687/) (2026)

## Abstract (arXiv)

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.