Quantum Diffusion Models for Few-Shot Learning

Ruhan Wang; Ye Wang; Jing Liu; Toshiaki Koike-Akino

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arXiv · 2411.04217

Quantum Diffusion Models for Few-Shot Learning

Ruhan Wang·Ye Wang·Jing Liu·Toshiaki Koike-Akino

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Abstract

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.

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Ruhan Wang, Ye Wang, Jing Liu, Toshiaki Koike-Akino. 2024-11-06. Quantum Diffusion Models for Few-Shot Learning. https://arxiv.org/abs/2411.04217

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