White paper on Quantum Computing and Quantum Machine Learning for Climate Science
Climate change stands as one of the most formidable challenges of the 21st century, requiring innovative approaches to address the complexity of modeling the Earth’s climate system, predicting future changes, and devising sustainable solutions. As classical computing approaches its limits in handling the intricate dynamics of climate systems, Quantum Computing (QC) and Quantum Machine learning (QML) are emerging as transformative technologies. These technologies offer the potential to revolutionize climate change efforts by introducing unprecedented computational power and advanced algorithms.
This white paper assesses the current state of QC and QML in climate-related research. It explores key applications, technological advancements, challenges, and future directions, with a particular emphasis on critical areas such as climate modeling, climate data forecasting, extreme event prediction, energy systems optimization, materials discovery, and predictive analytics. Additionally, it explores the current limitations, future directions, and the anticipated impact of quantum technologies in mitigating and adapting to climate change.
Clearly, the aim of this White Paper is not to provide comprehensive overview of quantum computing and quantum machine learning—an unfeasible task given the breadth and complexity of the topics involved. Instead, its purpose is to highlight key aspects of the field and its application to climate change, also offering guidance on scientific journals that can be consulted for further, in-depth exploration of the subject.