Quantum computing offers transformative potential for climate modeling by leveraging quantum mechanical principles to address computational bottlenecks in classical systems. This article synthesizes advancements from 2023–2025, focusing on molecular simulations, atmospheric modeling, optimization, and quantum machine learning (QML). Key results include a 40% improvement in carbon capture material efficiency, accelerated solutions to Navier-Stokes equations, and 92% accuracy in flood prediction using quantum support vector machines (QSVMs). Challenges such as limited coherence times, data throughput constraints, and hybrid system integration persist. Near-term applications with noisy intermediate-scale quantum (NISQ) devices and long-term prospects with fault-tolerant systems are explored, emphasizing interdisciplinary collaboration to align quantum solutions with climate challenges.
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