Quantum computing for Earth system modeling

Quantum computing represents a paradigm shift in computational science, with maturing hardware raising the prospect of significant advances across scientific domains. Earth system modeling requires simulating complex, multi-scale systems that often exceed classical computational limits, yet remains essential for developing effective mitigation and adaptation strategies. While classical machine learning has already become a cornerstone for accelerating and refining these simulations, the climate science community must now prepare to leverage emerging quantum technologies.

This keynote explores the integration of quantum computing into Earth system modeling. We examine how quantum machine learning (QML) can accurately parameterize subgrid-scale processes. We also present quantum algorithms for automated model tuning and discuss their potential to solve governing partial differential equations (PDEs) with greater efficiency. Finally, we outline a framework for incorporating the QML approaches into hybrid models, charting a pathway towards quantum-enhanced Earth system modeling.