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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

A Study on Quantum Machine Learning for Accurate and Efficient Weather Prediction

B. Surendiran, K Dhanasekaran, A Tamizhselvi

Published Nov 10, 2022• 8 citations
Publisher

Abstract

Recently Quantum Computing has gained much attention in the field of data science and computational problem solving. It is expected that the quantum machine learning will help researchers to find solutions for many complex problems in areas such as weather forecasting, data science, computational biology, energy management, secure communication, and many others. This paper presents a study on quantum machine learning techniques, challenges and applications of these techniques in climate change prediction, and weather forecasting towards future research in Quantum Machine Learning and Quantum Computing. It also discusses the latest developments and trends in Quantum machine Learning and presents practical examples to understand how Quantum Machine Learning considerably improves the performances of existing machine learning approaches.

DOI
10.1109/i-smac55078.2022.9987293

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The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.
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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