Quantum Multimodal Contrastive Learning Framework
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912258224816128 |
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| author | Chen, Chi-Sheng Tsai, Aidan Hung-Wen Huang, Sheng-Chieh |
| author_facet | Chen, Chi-Sheng Tsai, Aidan Hung-Wen Huang, Sheng-Chieh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Multimodal Contrastive Learning Framework Chen, Chi-Sheng Tsai, Aidan Hung-Wen Huang, Sheng-Chieh Quantum Physics Neurons and Cognition 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. |
| title | Quantum Multimodal Contrastive Learning Framework |
| topic | Quantum Physics Neurons and Cognition |
| url | https://arxiv.org/abs/2408.13919 |