7 papers · 1 filter
Shadow models of a quantum model for cloud cover and the influence of finite sampling noise
Hedwig Keller, Mierk Schwabe, Veronika Eyring
Quantum computing is a quickly growing field that is promising various advantages compared to conventional computing. However, currently stand-alone quantum applications are scarce…
How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello +7
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalab…
Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Nils Klement, Veronika Eyring, Mierk Schwabe
Partial differential equations (PDEs) form the backbone of simulations of many natural phenomena, for example in climate modeling, material science, and even financial markets. The…
Quantum Bayesian Optimization for the Automatic Tuning of Lorenz-96 as a Surrogate Climate Model
Paul J. Christiansen, Daniel Ohl de Mello, Cedric Brügmann +6
In this work, we propose a hybrid quantum-inspired heuristic for automatically tuning the Lorenz-96 model -- a simple proxy to describe atmospheric dynamics, yet exhibiting chaotic…
Quantum Machine Learning for Climate Modelling
Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea +1
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 th…
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models
Lorenzo Pastori, Arthur Grundner, Veronika Eyring +1
Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizont…