Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets. Most state-of-the-art QML algorithms currently lack practical advantages due to their limited learning capabilities, especially in few-shot learning tasks. In this work, we propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning: label-guided generation inference (LGGI); label-guided denoising inference (LGDI); and label-guided noise addition inference (LGNAI). Experimental results demonstrate that our proposed algorithms significantly outperform existing methods.
@article{arxiv.2411.04217,
title = {Quantum Diffusion Models for Few-Shot Learning},
author = {Ruhan Wang and Ye Wang and Jing Liu and Toshiaki Koike-Akino},
journal= {arXiv preprint arXiv:2411.04217},
year = {2024}
}