# Quantum-inspired Multimodal Fusion for Video Sentiment Analysis

arXiv: https://arxiv.org/abs/2103.10572 · HTML: https://mlsanity.com/paper/2103.10572/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 13 of 13)

- [Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations](https://mlsanity.com/paper/2210.17444/) (2022)
- [Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.01797/) (2021)
- [Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2111.08451/) (2021)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos](https://mlsanity.com/paper/2301.01134/) (2022)
- [Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis](https://mlsanity.com/paper/2504.12151/) (2025)
- [Expressive and Scalable Quantum Fusion for Multimodal Learning](https://mlsanity.com/paper/2510.06938/) (2025)
- [Curriculum Learning Meets Weakly Supervised Modality Correlation Learning](https://mlsanity.com/paper/2212.07619/) (2022)
- [Kernel Density Matrices for Probabilistic Deep Learning](https://mlsanity.com/paper/2305.18204/) (2023)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [Cooperative Sentiment Agents for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2404.12642/) (2024)
- [Bayesian statistical learning using density operators](https://mlsanity.com/paper/2212.14715/) (2022)
- [QiNN-QJ: A Quantum-inspired Neural Network with Quantum Jump for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.27091/) (2025)

## Abstract (arXiv)

We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.