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.
Journal reference
2025 IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, 2025, pp. 73-78
Accepted at IEEE Quantum Artificial Intelligence 2025 conference. This version is a pre-publication and may differ from the final IEEE-published version. The final paper will be available in the IEEE Xplore digital library
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.
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 horizontal) due to their high computational costs. Unresolved subgrid-scale processes, such as clouds, are described in a semi-empirical manner by so called parameterizations, which are a major source of uncertainty in climate projections. Machine learning models trained on short high-resolution climate simulations are promising candidates to replace conventional parameterizations. In this work, we explore the potential of quantum machine learning (QML), and in particular quantum neural networks (QNNs), to develop cloud cover parameterizations. QNNs differ from their classical counterparts, and their potentially high expressivity turns them into promising tools for accurate data-driven schemes to be used in climate models. We perform an extensive comparative analysis between several QNNs and classical neural networks (NNs), by training both ansatzes on data coming from high-resolution simulations with the ICOsahedral Non-hydrostatic weather and climate model (ICON). Our results show that the overall performance of the investigated QNNs is comparable to that of classical NNs of similar size, i.e., with the same number of trainable parameters, with both ansatzes outperforming standard parameterizations used in climate models. Our study includes an analysis of the generalization ability of the models as well as the geometrical properties of their optimization landscape. We also investigate the effects of finite sampling noise, and show that the training and the predictions of the QNNs are stable even in this noisy setting. These results demonstrate the applicability of QML to learn meaningful patterns in climate data, and are thus relevant for a broad range of problems within the climate modeling community.
Cloud cover parameterizations play an important role in climate models, influencing radiative transfer, atmospheric dynamics, and hydrological cycles. However, their correct formulation remains a major challenge in climate modeling, as subgrid-scale cloud processes must be approximated. Classical and quantum Neural Networks have been proposed as potential tools for improving these parameterizations. While previous studies suggest that classical neural networks can produce physically meaningful results, it remains unclear whether quantum neural networks (QNNs) exhibit similar capabilities or rely on spurious correlations. This study begins by comparing a classical neural network and a quantum neural network to assess whether they capture comparable physical dependencies when predicting cloud cover, before exploring alternative architectures for each approach. Using explainable AI (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), the learned feature dependencies in both types of models are analyzed. This approach enables us to evaluate not only predictive performance but also the extent to which each model captures the underlying physics of cloud cover. Our results show that both classical and quantum models exhibit similar learning patterns, extracting comparable relationships from the data. While the QNN does not outperform the classical network, it achieves comparable results, suggesting that quantum machine learning (QML) could be a viable approach in this domain. These findings contribute to the ongoing exploration of QML in climate science and highlight the potential of quantum methods for atmospheric modeling. More broadly, this study supports the integration of machine learning into climate science while ensuring physical consistency and interpretability.
Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This is mainly due to uncertainties in the representation of subgrid-scale processes such as turbulence or convection that are partly alleviated at higher resolution. New developments in machine learning-based hybrid ESMs demonstrate great potential for systematically reduced errors compared to traditional ESMs. Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimisation problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. We also discuss hurdles and obstacles facing current quantum computing paradigms. Strong interdisciplinary collaboration between climate scientists and quantum computing experts could help overcome these hurdles and harness the potential of quantum computing for this urgent topic.
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 scalability, partly due to phenomena such as barren plateaus. In this paper, we employ a hybrid quantum neural network (QNN) on a dataset on cloud microphysics, containing processes for phase transitions of water in the atmosphere and its related temperature changes, which are highly relevant for accurate climate predictions and projections. To reach optimal performance of our QNNs, we employ a rich and trainable frequency spectrum together with expressivity enhancing classical postprocessing. We find that our QNNs strongly benefit from extensive hyperparameter optimization and thereby demonstrate the feasibility of applying QNNs to complex physical systems. At the same time, the QNNs are outperformed by classical baselines in the form of simple fully-connected neural networks. We discuss identified bottlenecks of this class of quantum models to learn the full complexity of the cloud microphysics dataset to show that there is a need to further understand and improve variational quantum models for machine learning such that they might fill the gap where classical models fail or are inefficient.
Abstract Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This is mainly due to uncertainties in the representation of subgrid-scale processes such as turbulence or convection that are partly alleviated at higher resolution. New developments in machine learning-based hybrid ESMs demonstrate great potential for systematically reduced errors compared to traditional ESMs. Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimization problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. We also discuss hurdles and obstacles facing current quantum computing paradigms. Strong interdisciplinary collaboration between climate scientists and quantum computing experts could help overcome these hurdles and harness the potential of quantum computing for this urgent topic.
Current climate models often struggle with accuracy because they lack sufficient resolution, a limitation caused by computational constraints. This reduces the precision of weather forecasts and long-term climate predictions. To address this issue, we explored the use of quantum computing to enhance traditional machine learning (ML) models. We replaced conventional models like Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Encoder-Decoder frameworks with their quantum versions: Quantum Convolutional Neural Networks (QCNN), Quantum Multilayer Perceptrons (QMLP), and Quantum Encoder-Decoders (QED). These quantum models proved to be more accurate in predicting climate-related outcomes compared to their classical counterparts. Using the ClimSim dataset, a large collection of climate data created specifically for ML-based climate prediction, we trained and tested these quantum models. Individually, the quantum models performed better, but their performance was further improved when we combined them using a meta-ensemble approach, which merged the strengths of each model to achieve the highest accuracy overall. This study demonstrates that quantum machine learning can significantly improve the resolution and accuracy of climate simulations. The results offer new possibilities for better predicting climate trends and weather events, which could have important implications for both scientific understanding and policy-making in the face of global climate challenges.