Quantum Diffusion Models for Few-Shot Learning
This work addresses few-shot learning challenges in quantum machine learning, but it appears incremental as it builds on existing quantum diffusion model concepts.
The paper tackled the problem of limited learning capabilities in quantum machine learning for few-shot learning tasks by proposing three new frameworks using quantum diffusion models, and the result was that these algorithms significantly outperformed existing methods, though no concrete numbers were provided.
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.