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arXiv:2210.17460 · Bulletin of the American Meteorological Society

Quantum Computers for Weather and Climate Prediction: The Good, the Bad, and the Noisy

Felix Tennie, T. N. Palmer

Published Jan 18, 2023• 56 citations
arXivHTMLPDF

Abstract

Abstract Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a nontechnical yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving nonlinear differential equations, we integrate a simple nonlinear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving “big data,” such as weather prediction.

DOI
10.1175/bams-d-22-0031.1

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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.
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Global challenge of climate change demands precise predictive models to grasp its effects and create sensible mitigating plans. Based on classical computing methods, traditional models suffer in processing complicated climate data, which results in lower accuracy in long-term forecasts and extreme weather prediction. With its capacity to do huge parallel computations via superposition and entanglement, quantum computing offers a potential fix for improving climate models. In addition to focused on its ability to increase weather forecasting accuracy, long-term climate projections, and extreme weather event predictions, this work investigates the integration of quantum computing in climate science. Quantum-assisted climate models demonstrate better computational efficiency, higher forecast accuracy, and better resource management for mitigating techniques including sustainable agriculture, carbon capture, and solar energy deployment. However, obstacles, developments in quantum technology and multidisciplinary cooperation between climate researchers and quantum physicists are opening the path for more dependable and scalable climate solutions.
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