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.
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.
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.
Climate prediction plays a crucial role in understanding and mitigating the impacts of climate change. Traditional climate models, though powerful, often face challenges in handling the vast, high-dimensional satellite imagery data required for accurate forecasting. Meanwhile, quantum computing offers new computational paradigms capable of addressing complex problems intractable for classical systems. In this research, we propose an innovative framework for an image-based climate prediction model leveraging quantum computing technology. By integrating quantum machine learning algorithms with satellite Earth observation data, the model aims to extract critical climatic patterns such as temperature anomalies, cloud movements, and vegetation dynamics with enhanced precision and computational efficiency. The study addresses existing limitations by developing specialized quantum algorithms for image processing, exploring quantum-classical hybrid architectures, and optimizing the system for real-world scalability using current and near-term quantum devices. Preliminary results suggest that quantum approaches have the potential to outperform traditional techniques in processing speed and predictive accuracy, particularly for high-resolution, time-series satellite imagery. This work represents a significant step toward practical, quantum-enhanced climate modeling, bridging the gap between quantum computing advancements and urgent global climate forecasting needs.
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.
This chapter discusses the transformative potential of quantum computing in the forecasting of weather and modeling of climate—two very critical fields for understanding and addressing climate change. Quantum computing brings unique computational advantages that will improve the accuracy of predictions, increase knowledge about climate systems, and informed decision-making. The chapter first discusses the importance of these fields and introduces quantum computing and its many applications. It then outlines the current methods used in weather forecasting and climate modeling, highlighting the limitations of traditional computational approaches. Key quantum computing concepts, such as qubits, superposition, and entanglement, are discussed alongside quantum algorithms and architectures. This chapter addresses how quantum computing could bring numerical weather prediction models to a new level, allowing for faster, more accurate weather forecasting through quantum simulation, and using quantum machine learning in analyzing weather patterns. It also gives quantum-inspired algorithms for climate modeling and optimization problems related to 174processing large climatic datasets. While highlighting the promise of quantum computing in revolutionizing these domains, the chapter also discusses its limitations: technical, ethical, and societal. It, therefore, calls for collaboration and responsible deployment in order to reap the full potential of quantum computing to improve the accuracy of predictions, understand climate dynamics, and reduce the impacts of climate change.
The advent of quantum computing has opened unprecedented promises to tackle humanity' s most pressing challenges: computationally intractable problems might come within reach through quantum advantages. It is imperative for the field of climate modeling to explore possible uses of this emerging technology. Global climate models have successfully projected different future scenarios, but the spread in projections remains large, with subgrid-scale parameterizations being the main origin of these uncertainties. While machine learning models improve these parameterizations, quantum computing could bring decisive further improvements. Atmospheric turbulence affects the climate because it determines the rates of exchange of heat, moisture, and momentum between the Earth surface and the atmosphere, but has been especially hard to predict and model. Here, we develop a quantum machine learning-based subgrid-scale parameterization for the turbulent vertical heat flux. The training data are coarse-grained data from large-eddy-simulations of a dry convective boundary layer in an idealized setup without topography. Training various quantum and classical neural network models, we find that quantum models based on parametrized circuits with just 2 or 3 qubits achieve accuracies similar to classical models with the same number of trainable parameters. In contrast, the Smagorinsky closure applied to the coarse target resolution deviates strongly from the highly resolved flux. Both our quantum and classical models generalize well to unseen atmospheric conditions. Finally, the feature importances in our quantum models are on average more stable with respect to the random initial parameters, highlighting the possibility to exploit quantum advantages in climate and Earth system models.
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.