Quantum Machine Learning in Climate Change and Sustainability: a Review

TL;DR

This review explores how quantum machine learning can be applied to climate change and sustainability challenges, highlighting current methods, limitations, and future opportunities for leveraging quantum computing in environmental research.

Contribution

It provides a comprehensive survey of QML applications in climate and sustainability, identifying promising methodologies and discussing challenges and future directions.

Findings

QML can accelerate climate data analysis and forecasting

Current limitations include hardware constraints and algorithm scalability

Future opportunities involve integrating QML with existing climate models

Abstract

Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.