Quantum Artificial Intelligence for the Science of Climate Change
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Abstract (arXiv)
Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability. Recent developments such as the extraordinary heat waves in California & Canada, and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of the weather and climate have seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing the loss of life and property. Artificial Intelligence-based methods are demonstrating promising results in improving predictions, but are still limited by the availability of requisite hardware and software required to process the vast deluge of data at a scale of the planet Earth. Quantum computing is an emerging paradigm that has found potential applicability in several fields. In this opinion piece, we argue that new developments in Artificial Intelligence algorithms designed for quantum computers - also known as Quantum Artificial Intelligence (QAI) - may provide the key breakthroughs necessary to furthering the science of climate change. The resultant improvements in weather and climate forecasts are expected to cascade to numerous societal benefits.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 28 Jul 2021.
Builds on
13 of 27 references.
References · 13
- 2020Power of data in quantum machine learningcited by 336
- 2019Tackling Climate Change with Machine Learningcited by 264
- 2018Deep Learning: A Critical Appraisalcited by 214
- 2019Efficient Learning for Deep Quantum Neural Networkscited by 160
- 2018Deep learning to represent sub-grid processes in climate modelscited by 156
- 2020Improving data-driven global weather prediction using deep convolutional neural networks on a cubed spherecited by 95
- 2019Digital Twin: Values, Challenges and Enablerscited by 81
- 2020A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantagecited by 23
- 2020Quantum Computing: A Taxonomy, Systematic Review and Future Directionscited by 16
- 2020When Machine Learning Meets Quantum Computers: A Case Studycited by 9
- 2021Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classificationcited by 9
- 2021Deep learning for improved global precipitation in numerical weather prediction systemscited by 5
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Citing papers per year: 2023: 1.
1 paper.