An Evaluation of FRQI and NEQR Encoding Using QCNN for Forecasting Tropical Cyclone Intensity

Abstract

Effective weather forecasting for cyclones is crucial for minimizing harm to both people and the environment. Accurate estimation of tropical cyclone (TC) intensity is essential for disaster prevention. Although convolutional neural networks (CNNs) have improved this process, they often struggle to capture global spatial relationships in images. Quantum Image Processing (QIP) leverages quantum computing advantages but faces challenges such as noise and hardware limitations. This study represents the first effort to estimate tropical cyclone intensity prediction using two popular quantum image representation formats: Flexible Representation of Quantum Images (FRQI) and a Novel Enhanced Quantum Representation (NEQR), as data encoders in Quantum Convolutional Neural Networks (QCNN) utilizing INSAT 3D satellite images.  By employing TCs from 2012 to 2021 as training data, the model achieved an overall mean square error (MSE) of 0.0384 for FRQI  and 0.0002 for NEQR. The findings indicate that NEQR significantly outperforms FRQI in cyclone image prediction.