The Evolution of Adaptive Multi-Modal Data Fusion for Quantum Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces an adaptive multi-modal data fusion framework designed to enhance the predictive accuracy and generalization capabilities of quantum machine learning models. Traditional multi-modal data fusion methods often rely on static data fusion strategies, limiting their effectiveness in complex datasets. Our framework dynamically adjusts data fusion strategies, enabling a more flexible and robust approach to model performance. We demonstrate the framework's efficacy through a series of simulations and analyses, highlighting its ability to improve performance across diverse tasks. The core mechanism involves an adaptive algorithm that continuously evaluates and adjusts the fusion process based on the data itself. This iterative refinement ensures optimal data integration, ultimately contributing to superior model outcomes.

DOI: 10.5281/zenodo.22299363

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