Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery

TL;DR

This paper introduces a Quantum-Enhanced Vision Transformer that combines quantum computing with deep learning to improve flood detection accuracy from remote sensing images, outperforming classical models.

Contribution

The paper presents a novel hybrid quantum-classical architecture for flood detection, integrating quantum circuits with vision transformers for enhanced feature extraction.

Findings

Accuracy increased from 84.48% to 94.47%.

F1-score improved from 0.841 to 0.944.

Quantum integration enhanced discriminative power.

Abstract

Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.