A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction

TL;DR

This paper introduces a novel hybrid quantum-classical neural network model that combines graph convolutional networks with quantum multilayer perceptrons to improve the prediction of the Oceanic Nino Index, demonstrating potential to outperform current methods.

Contribution

The paper presents a new hybrid quantum-classical neural network architecture specifically designed for complex climate index prediction, integrating quantum computing with graph neural networks.

Findings

Preliminary results indicate the model can surpass state-of-the-art performance.

The approach effectively captures global dependencies in climate data.

The code will be publicly available for further research.

Abstract

This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP). The study highlights the potentialities of GCNNs in handling global-scale dependencies and proposes the HQGCNN for predicting complex phenomena such as the Oceanic Nino Index (ONI). Preliminary results suggest the model potential to surpass state-of-the-art (SOTA). The code will be made available with the paper publication.