Quantum Diffusion Models for Few-Shot Learning

• URL: http://arxiv.org/abs/2411.04217v1
• Date: Wed, 06 Nov 2024 19:25:06 GMT
• Title: Quantum Diffusion Models for Few-Shot Learning
• Authors: Ruhan Wang, Ye Wang, Jing Liu, Toshiaki Koike-Akino,
• Abstract summary: We propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning. Experimental results demonstrate that our proposed algorithms significantly outperform existing methods.
• Score: 13.13788757618812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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