Quantum Machine Learning for Climate Modelling

TL;DR

This paper explores the application of quantum neural networks to climate modeling, specifically for predicting cloud cover, demonstrating comparable or superior performance to classical neural networks and improved learning consistency.

Contribution

It introduces a quantum neural network approach for climate model parameterization, showing its effectiveness and potential advantages over classical methods.

Findings

QNN predicts cloud cover as well as classical NN with same parameters

QNN outperforms traditional schemes in accuracy

QNN learns more consistent relationships than classical NNs

Abstract

Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability than their classical counterparts. Here, we present work on using a quantum neural net (QNN) to develop a parameterization of cloud cover for an Earth system model (ESM). ESMs are needed for predicting and projecting climate change, and can be improved in hybrid models incorporating both traditional physics-based components as well as machine learning (ML) models. We show that a QNN can predict cloud cover with a performance similar to a classical NN with the same number of free parameters and significantly better than the traditional scheme. We also analyse the learning capability of the QNN in comparison to the classical NN and show that, at least for our example, QNNs learn more consistent relationships than classical NNs.