Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.

翻译：气候变化及其对全球可持续发展的影响是当前面临的严峻挑战，亟需结合前沿技术与科学洞察的创新解决方案。量子机器学习作为一种新兴范式，通过利用量子计算的优势，为解决气候变化与可持续发展等领域的复杂问题提供了新途径。本文系统梳理了将量子机器学习应用于气候变化及可持续发展相关问题的现有文献，重点评述了能够加速脱碳进程的潜在QML方法，涵盖能源系统优化、气候数据预测、气候监测及灾害事件预警等领域。我们深入探讨了量子机器学习方法目前面临的挑战与局限性，并展望了未来在气候变化研究这一重要领域基于QML方法的潜在机会与发展方向。