Quantum Machine Learning for Climate Modelling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866910004088406016 |
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| author | Schwabe, Mierk Pastori, Lorenzo Sarandrea, Valentina Eyring, Veronika |
| author_facet | Schwabe, Mierk Pastori, Lorenzo Sarandrea, Valentina Eyring, Veronika |
| contents | 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 than their classical counterparts. Here, we present work on using a quantum neural net (QNN) to develop a parameterization of cloud cover for an Earth system model (ESM). ESMs are needed for predicting and projecting climate change, and can be improved in hybrid models incorporating both traditional physics-based components as well as machine learning (ML) models. We show that a QNN can predict cloud cover with a performance similar to a classical NN with the same number of free parameters and significantly better than the traditional scheme. We also analyse the learning capability of the QNN in comparison to the classical NN and show that, at least for our example, QNNs learn more consistent relationships than classical NNs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning for Climate Modelling Schwabe, Mierk Pastori, Lorenzo Sarandrea, Valentina Eyring, Veronika Quantum Physics Atmospheric and Oceanic Physics 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 than their classical counterparts. Here, we present work on using a quantum neural net (QNN) to develop a parameterization of cloud cover for an Earth system model (ESM). ESMs are needed for predicting and projecting climate change, and can be improved in hybrid models incorporating both traditional physics-based components as well as machine learning (ML) models. We show that a QNN can predict cloud cover with a performance similar to a classical NN with the same number of free parameters and significantly better than the traditional scheme. We also analyse the learning capability of the QNN in comparison to the classical NN and show that, at least for our example, QNNs learn more consistent relationships than classical NNs. |
| title | Quantum Machine Learning for Climate Modelling |
| topic | Quantum Physics Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2512.14208 |