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Quantum Computing and Quantum Machine Learning for Earth System Models

Pastori, Lorenzo and Schwabe, Mierk and Eyring, Veronika (2023) Quantum Computing and Quantum Machine Learning for Earth System Models. Quantum Information Processing – Applications on Gate-based an Annealing Systems, 2023-08-28 - 2023-09-01, Jülich, Deutschland.

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Long-term climate projections require running global Earth system models on timescales of hundreds of years and have, due to their complexity and high computational costs, relatively coarse resolution (40-100 km in the horizontal direction). Unresolved sub-grid scale processes, such as clouds, convection or turbulence, are described in a statistical manner by so called parametrizations. Parametrizations are still one of the major sources of uncertainty in climate projections, owing to systematic model discrepancies as well as possibly sub optimal choices of the underlying free parameters. In the last years, there has been increasing focus on using machine learning (ML) methods to address these challenges. While enabling several improvements, the use of ML also highlights several requirements such as the ability to generalize to unseen physical scenarios, along with the need of robust and efficient multi-parameter optimization routines. Quantum computing offers alternative paradigms, which are promising candidates for addressing these challenges. Quantum machine learning (QML) models could benefit from better expressivity and generalization capability compared to classical ML, hence constituting promising Ansätze for accurate parametrizations in Earth system models. Quantum optimization algorithms such as quantum annealing are expected to provide substantial speed-ups compared to classical routines. They are thus suited as optimization subroutines in quantum-enhanced protocols for model tuning, a currently very timeconsuming step in the development of Earth system models. Here, we discuss these ideas and highlight opportunities and challenges in the use of quantum computers for improving climate and Earth system models and their analysis, and accelerating their development.

Item URL in elib:https://elib.dlr.de/199461/
Document Type:Conference or Workshop Item (Poster)
Title:Quantum Computing and Quantum Machine Learning for Earth System Models
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pastori, LorenzoDLR, IPAhttps://orcid.org/0000-0001-5882-8482147716739
Schwabe, MierkDLR, IPAhttps://orcid.org/0000-0001-6565-5890UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:quantum computing, quantum machine learning, climate models, earth system models
Event Title:Quantum Information Processing – Applications on Gate-based an Annealing Systems
Event Location:Jülich, Deutschland
Event Type:Workshop
Event Start Date:28 August 2023
Event End Date:1 September 2023
Organizer:Jülich Supercomputing Centre
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research, R - Quantum computing
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Pastori, Lorenzo
Deposited On:29 Nov 2023 15:07
Last Modified:24 Apr 2024 21:00

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