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.
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.
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.
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.
Pattern identification for weather and climate research can benefit from quantum machine learning. The issues that will benefit the most from quantum speedup are those that are naturally parallelizable. Future predictions indicate that quantum artificial intelligence will be a potent technology. Computer vision and sequencing algorithms that can be used on powerful quantum computers are among the advancements in the field of quantum artificial intelligence. One component, namely the creation of high performance quantum computing hardware, would be the driving force behind all these developments. For huge N, a quantum processor's simulation capabilities seem to be simply unmatched by their conventional equivalent, which might allow for the resolution of difficult and technologically relevant physics and chemistry problems. Assuming that feasible large -scale devices can be constructed and installed in a reasonable length of time, the objective of quantum computers is not merely to speed up present calculations. Models based on partial differential equations only explicitly resolve physical processes operating at spatial scales greater than the grid size; processes operating at smaller scales, such as clouds and deep convection, are represented by approximate empirical relationships known as parameterizations. It is still unclear i) to what extent quantum computers can offer optimisation advantages over classical algorithms in important applications of interest, and ii) when we can expect to realise such advantages in terms of both hardware and algorithm development. This is true even though optimisation offers a wide range of potential quantum computing applications related to climate change. This review paper analyses the three types of quantum processes that can be used to solve climate issues. Some of them are Quantum Simulation, Quantum Optimization and Quantum Sensing.
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 is accelerating, necessitating sophisticated forecasting techniques to alleviate its impacts efficiently. The huge amount and diversity of climate data frequently exceed the endurance of traditional computing techniques, creating problems with accuracy and processing speed. This research presents a quantum-enhanced framework for big data analytics in climate science by combining quantum machine learning, optimisation, and simulation methods. The recommended strategy empowers specific analysis of high-dimensional climate datasets, contributing noteworthy enhancements in predicting critical phenomena such as temperature irregularities, rainfall trends, and catastrophic weather patterns. By including quantum algorithms, the framework attains scalability and real-time competencies, minimising computational processing times while improving predictive accuracy. These results demonstrate the advantage of quantum-enhanced models over classical approaches by giving innovative tools for policymakers and industry leaders to help them make data-driven, well-informed decisions. The study highlights the revolutionary potential of quantum computing in addressing global climate challenges, making new paths for environmentally friendly remedies. As quantum technologies develop, their use in climate science will motivate innovative solutions for the endurance and preservation of the environment. Major Findings: This study highlights the necessity for cross-disciplinary collaboration to completely integrate quantum computing advancements into climate modelling, guaranteeing actionable and accurate perceptions. The results show how important quantum-enhanced analytics can be in helping to develop sustainable decision-making to deal with the escalating climate crisis.