Expressive and Scalable Quantum Fusion for Multimodal Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914081353498624 |
|---|---|
| author | Nguyen, Tuyen Hoang, Trong Nghia Nguyen, Phi Le Vu, Hai L. Thang, Truong Cong |
| author_facet | Nguyen, Tuyen Hoang, Trong Nghia Nguyen, Phi Le Vu, Hai L. Thang, Truong Cong |
| contents | The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Quantum Fusion Layer (QFL), replaces classical fusion schemes with a hybrid quantum-classical procedure that uses parameterized quantum circuits to learn entangled feature interactions without requiring exponential parameter growth. Supported by quantum signal processing principles, the quantum component efficiently represents high-order polynomial interactions across modalities with linear parameter scaling, and we provide a separation example between QFL and low-rank tensor-based methods that highlights potential quantum query advantages. In simulation, QFL consistently outperforms strong classical baselines on small but diverse multimodal tasks, with particularly marked improvements in high-modality regimes. These results suggest that QFL offers a fundamentally new and scalable approach to multimodal fusion that merits deeper exploration on larger systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Expressive and Scalable Quantum Fusion for Multimodal Learning Nguyen, Tuyen Hoang, Trong Nghia Nguyen, Phi Le Vu, Hai L. Thang, Truong Cong Quantum Physics Artificial Intelligence The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Quantum Fusion Layer (QFL), replaces classical fusion schemes with a hybrid quantum-classical procedure that uses parameterized quantum circuits to learn entangled feature interactions without requiring exponential parameter growth. Supported by quantum signal processing principles, the quantum component efficiently represents high-order polynomial interactions across modalities with linear parameter scaling, and we provide a separation example between QFL and low-rank tensor-based methods that highlights potential quantum query advantages. In simulation, QFL consistently outperforms strong classical baselines on small but diverse multimodal tasks, with particularly marked improvements in high-modality regimes. These results suggest that QFL offers a fundamentally new and scalable approach to multimodal fusion that merits deeper exploration on larger systems. |
| title | Expressive and Scalable Quantum Fusion for Multimodal Learning |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.06938 |