Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Chi-Sheng Chen, Aidan Hung-Wen Tsai, Sheng-Chieh Huang
Institution: National Yang Ming Chiao Tung University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408324858