Quantum-Enhanced Water Quality Monitoring: Exploiting $\Phi$ Sat-2 Data With Quanvolution

Abstract

Coastal water quality monitoring is crucial for environmental sustainability and public health. This work introduces a very cutting-edge methodology, using $\Phi $ Sat-2 multispectral data and quanvolutional neural networks (QNNs) to explore quantum-enhanced machine learning (ML) for water contaminant assessment. By integrating quantum preprocessing into a classical regression model, it is possible to achieve a significant reduction in model parameters while maintaining high predictive accuracy. In addition, this work introduces an innovative dataset that integrates simulated $\Phi $ Sat-2 spectral data with Copernicus Marine Service biogeochemical products, ensuring a strong alignment between satellite observations and reference turbidity measurements. Our results show that quantum models use up to 98% fewer parameters than their classical counterparts, while achieving a 6.9% improvement in the Pearson correlation coefficient between the $\Phi $ Sat-2 preprocessed bands and the ground-truth turbidity values, compared with the case without quantum preprocessing. In addition, the root mean square error (RMSE) improves by 7.3% over the classical baseline. These findings highlight the potential of quantum-assisted remote sensing (RS) to enable more efficient and scalable analysis of large-scale water contaminant data, paving the way for advanced big data approaches in water quality monitoring.