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International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Quantum Computing for Climate Change: A Comprehensive Review of Current Applications, Challenges, and Future Directions

Siddhant Ashwani, Aayushi Jeeban Tripathy, Siwani Karna, Poluru Reddy Jahanve, Shinu M. Rajagopal

Published Jun 24, 2024• 12 citations
Publisher

Abstract

The need for creative ideas to address the growing challenges posed by climate change has become critical. This paper explores the various uses of quantum computing for mitigating climate change, with an emphasis on energy optimization, improved climate modeling, and weather forecasting. Environmental modeling, chemistry, material science, carbon capture techniques, and other significant applications have also been discussed. Finding important approaches, results, and associated technological constraints is the current state of study. Examining relevant case studies, the review suggests strategic directions for future research on climate change enabled by quantum computing. It serves as an essential tool for scientists, policymakers, and researchers, enabling a deeper comprehension of the potential of quantum computing in environmental science and encouraging multidisciplinary cooperation for the long-term mitigation of climate change.

DOI
10.1109/icccnt61001.2024.10724347

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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.
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