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Climate Change, Extreme Weather Prediction, Quantum Computing
Abstract
The background of this research focuses on the challenges in forecasting extreme weather that is increasingly frequent due to climate change. Conventional weather models still face limitations in terms of accuracy and computational time, especially in predicting extreme weather phenomena. The purpose of this study is to explore the potential of quantum computing in predicting extreme weather by improving prediction accuracy and accelerating computational processes. The research method used involves the development and testing of weather prediction models based on quantum algorithms on extreme weather phenomena such as tropical storms, heavy rains, and heat waves. The results show that the quantum model is able to improve prediction accuracy by up to 92% for tropical storms and accelerate the computational time from 48 hours to 5 hours. The conclusion of the study is that quantum computing offers a more efficient and accurate solution in forecasting extreme weather, with great potential for practical applications in early warning and mitigation of weather disasters.
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References
Ajagekar, A. (2021). Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems. Applied Energy, 303(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.apenergy.2021.117628
Ajagekar, A. (2022). Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable and Sustainable Energy Reviews, 165(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.rser.2022.112493
Ali, U. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.enbuild.2021.111073
Awan, U. (2022). Quantum computing challenges in the software industry. A fuzzy AHP-based approach. Information and Software Technology, 147(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.infsof.2022.106896
Bardin, J. C. (2021). Microwaves in Quantum Computing. IEEE Journal of Microwaves, 1(1), 403–427. https://doi.org/10.1109/JMW.2020.3034071
Bayerstadler, A. (2021). Industry quantum computing applications. EPJ Quantum Technology, 8(1). https://doi.org/10.1140/epjqt/s40507-021-00114-x
Blunt, N. S. (2022). Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications. Journal of Chemical Theory and Computation, 18(12), 7001–7023. https://doi.org/10.1021/acs.jctc.2c00574
Bravyi, S. (2022). The future of quantum computing with superconducting qubits. Journal of Applied Physics, 132(16). https://doi.org/10.1063/5.0082975
Burg, V. von. (2021). Quantum computing enhanced computational catalysis. Physical Review Research, 3(3). https://doi.org/10.1103/PhysRevResearch.3.033055
Campa, F. (2021). Assessment of body composition in athletes: A narrative review of available methods with special reference to quantitative and qualitative bioimpedance analysis. Nutrients, 13(5). https://doi.org/10.3390/nu13051620
Chen, X. (2022). Weather Radar Nowcasting for Extreme Precipitation Prediction Based on the Temporal and Spatial Generative Adversarial Network. Atmosphere, 13(8). https://doi.org/10.3390/atmos13081291
Emani, P. S. (2021). Quantum computing at the frontiers of biological sciences. Nature Methods, 18(7), 701–709. https://doi.org/10.1038/s41592-020-01004-3
Fan, J. (2021). Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting. Journal of Hydrology, 601(Query date: 2024-12-07 17:50:28). https://doi.org/10.1016/j.jhydrol.2021.126664
Fang, W. (2021). Survey on the application of deep learning in extreme weather prediction. Atmosphere, 12(6). https://doi.org/10.3390/atmos12060661
Gill, S. S. (2022). Quantum computing: A taxonomy, systematic review and future directions. Software - Practice and Experience, 52(1), 66–114. https://doi.org/10.1002/spe.3039
Gonzalez-Zalba, M. F. (2021). Scaling silicon-based quantum computing using CMOS technology. Nature Electronics, 4(12), 872–884. https://doi.org/10.1038/s41928-021-00681-y
Hegade, N. N. (2021). Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing. Physical Review Applied, 15(2). https://doi.org/10.1103/PhysRevApplied.15.024038
Herman, D. (2023). Quantum computing for finance. Nature Reviews Physics, 5(8), 450–465. https://doi.org/10.1038/s42254-023-00603-1
Kavokin, A. (2022). Polariton condensates for classical and quantum computing. Nature Reviews Physics, 4(7), 435–451. https://doi.org/10.1038/s42254-022-00447-1
Kim, Y. (2023). Evidence for the utility of quantum computing before fault tolerance. Nature, 618(7965), 500–505. https://doi.org/10.1038/s41586-023-06096-3
Kwon, S. (2021). Gate-based superconducting quantum computing. Journal of Applied Physics, 129(4). https://doi.org/10.1063/5.0029735
Leon, N. P. de. (2021). Materials challenges and opportunities for quantum computing hardware. Science, 372(6539). https://doi.org/10.1126/science.abb2823
Li, D. (2020). Nanosol SERS quantitative analytical method: A review. TrAC - Trends in Analytical Chemistry, 127(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.trac.2020.115885
Mangini, S. (2021). Quantum computing models for artificial neural networks. EPL, 134(1). https://doi.org/10.1209/0295-5075/134/10002
Marshall, A. G. (2022). Subseasonal drivers of extreme fire weather in Australia and its prediction in ACCESS-S1 during spring and summer. Climate Dynamics, 58(1), 523–553. https://doi.org/10.1007/s00382-021-05920-8
Michelini, S. (2023). Extreme weather impacts do not improve conflict predictions in Africa. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01996-1
Mosteanu, N. R. (2021). Fintech frontiers in quantum computing, fractals, and blockchain distributed ledger: Paradigm shifts and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–19. https://doi.org/10.3390/joitmc7010019
Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027
Nokkala, J. (2021). Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Communications Physics, 4(1). https://doi.org/10.1038/s42005-021-00556-w
Nooraie, R. Y. (2020). Social Network Analysis: An Example of Fusion Between Quantitative and Qualitative Methods. Journal of Mixed Methods Research, 14(1), 110–124. https://doi.org/10.1177/1558689818804060
O’Brien, W. (2020). Does telecommuting save energy? A critical review of quantitative studies and their research methods. Energy and Buildings, 225(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.enbuild.2020.110298
Ramhormozi, R. S. (2022). Multi-task graph neural network for truck speed prediction under extreme weather conditions. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Query date: 2024-12-07 17:50:28. https://doi.org/10.1145/3557915.3561029
Rasool, R. U. (2023). Quantum Computing for Healthcare: A Review. Future Internet, 15(3). https://doi.org/10.3390/fi15030094
Shanker, G. (2024). Relative economic value of global ensemble prediction system of NCMRWF, India, for extreme weather events. Quarterly Journal of the Royal Meteorological Society, 150(761), 1831–1846. https://doi.org/10.1002/qj.4674
Suzuki, Y. (2022). Quantum Error Mitigation as a Universal Error Reduction Technique: Applications from the NISQ to the Fault-Tolerant Quantum Computing Eras. PRX Quantum, 3(1). https://doi.org/10.1103/PRXQuantum.3.010345
Watson, P. L. (2022). Improved quantitative prediction of power outages caused by extreme weather events. Weather and Climate Extremes, 37(Query date: 2024-12-07 17:50:28). https://doi.org/10.1016/j.wace.2022.100487
Wu, Y. (2022). Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-32094-6
Zhi, X. (2023). Editorial: AI-based prediction of high-impact weather and climate extremes under global warming: A perspective from the large-scale circulations and teleconnections. Frontiers in Earth Science, 11(Query date: 2024-12-07 17:50:28). https://doi.org/10.3389/feart.2023.1126381
Zhu, F. (2024). Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Systems with Applications, 236(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.eswa.2023.121219
Zubaidi, S. L. (2023). Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: A case study in Melbourne, Australia. Applied Water Science, 13(9). https://doi.org/10.1007/s13201-023-01995-2
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Copyright (c) 2025 Vicheka Rith, Dara Vann, Luis Santos
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