mlsanityData to 14 Oct 2026

Expressive and Scalable Quantum Fusion for Multimodal Learning

arXiv 2510.06938 ↗First posted 8 Oct 2025quant-ph cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 78 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

33 of 78 references.

References · 33

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21 more
  1. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
  2. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026PQFA: Parallel Quantum Feature Augmentation of Fused Representations for Multimodal Classificationcited by 0
  2. 2026How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuitscited by 0