Expressive and Scalable Quantum Fusion for Multimodal Learning

• URL: http://arxiv.org/abs/2510.06938v1
• Date: Wed, 08 Oct 2025 12:19:44 GMT
• Title: Expressive and Scalable Quantum Fusion for Multimodal Learning
• Authors: Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen, Hai L. Vu, Truong Cong Thang,
• Abstract summary: The proposed method, called the Quantum Fusion Layer (QFL), replaces classical fusion schemes with a hybrid quantum-classical procedure.<n>In simulation, QFL consistently outperforms strong classical baselines on small but diverse multimodal tasks.<n>These results suggest that QFL offers a fundamentally new and scalable approach to multimodal fusion that merits deeper exploration on larger systems.
• Score: 16.82193084379014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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