# Quantum Multimodal Contrastive Learning Framework

arXiv: https://arxiv.org/abs/2408.13919 · HTML: https://mlsanity.com/paper/2408.13919/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2024.

## Built on by (most cited first, 10 of 10)

- [Quantum Adaptive Self-Attention for Quantum Transformer Models](https://mlsanity.com/paper/2504.05336/) (2025)
- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)
- [Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST](https://mlsanity.com/paper/2504.00034/) (2025)
- [EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG](https://mlsanity.com/paper/2506.00854/) (2025)
- [Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning](https://mlsanity.com/paper/2503.00080/) (2025)
- [Expressive and Scalable Quantum Fusion for Multimodal Learning](https://mlsanity.com/paper/2510.06938/) (2025)
- [Optimizing Supply Chain Networks with the Power of Graph Neural Networks](https://mlsanity.com/paper/2501.06221/) (2025)
- [Quantum Implicit Neural Compression](https://mlsanity.com/paper/2412.19828/) (2024)
- [Conditional Quantum Flow Matching for Data-Scarce Physiological Signal Augmentation](https://mlsanity.com/paper/2609.14019/) (2026)
- [Quantum Entangled Multimodal Fusion Networks (QEMFN): Resource-Aware Hybrid Vision-Language Fusion via Trainable Entanglement](https://mlsanity.com/paper/2610.08216/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreaking attempt explores the integration of quantum encoders within the traditional multimodal learning framework. By leveraging the unique properties of quantum computing, our method enhances the representation learning capabilities, providing a robust framework for analyzing time series and visual information concurrently. We demonstrate that the quantum encoder effectively captures intricate patterns within EEG signals and image features, facilitating improved contrastive learning across modalities. This work opens new avenues for integrating quantum computing with multimodal data analysis, particularly in applications requiring simultaneous interpretation of temporal and visual data.