Quantum Diffusion Models for Few-Shot Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909379376185344 |
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| author | Wang, Ruhan Wang, Ye Liu, Jing Koike-Akino, Toshiaki |
| author_facet | Wang, Ruhan Wang, Ye Liu, Jing Koike-Akino, Toshiaki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Diffusion Models for Few-Shot Learning Wang, Ruhan Wang, Ye Liu, Jing Koike-Akino, Toshiaki Machine Learning Artificial Intelligence 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. |
| title | Quantum Diffusion Models for Few-Shot Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.04217 |