REVOLUTIONIZING CLIMATE MODELING WITH QUANTUM COMPUTING AND MACHINE LEARNING

Climate modeling is a vital tool in the study and prediction of climate change and its repercussions.Conventional climate models are very productive, but their computing power and accuracy are constrained by the complexity of modeling several interacting systems over extended periods of time.With the ability to tackle complicated problems faster than traditional computers, quantum computing presents a viable way to improve climate models.Using a focus on quantum algorithms, hybrid quantum-classical techniques, and the integration of quantum machine learning, this research investigates how quantum computing may enhance the precision and efficacy of climate models.