TR2025-095
Quantum Diffusion Models for Few-Shot Learning
-
- "Quantum Diffusion Models for Few-Shot Learning", ICAD, DOI: 10.1109/ICAD65464.2025.11114033, June 2025.BibTeX TR2025-095 PDF
- @inproceedings{Wang2025jun2,
- author = {Wang, Ruhan and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
- title = {{Quantum Diffusion Models for Few-Shot Learning}},
- booktitle = {ICAD},
- year = 2025,
- month = jun,
- publisher = {IEEE},
- doi = {10.1109/ICAD65464.2025.11114033},
- isbn = {979-8-3315-2472-2},
- url = {https://www.merl.com/publications/TR2025-095}
- }
,
- "Quantum Diffusion Models for Few-Shot Learning", ICAD, DOI: 10.1109/ICAD65464.2025.11114033, June 2025.
-
MERL Contacts:
-
Research Areas:
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