Quantum Machine Learning in Climate Change and Sustainability: a Review
Amal Nammouchi Andreas Kassler Andreas Theorachis
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.