面向气候韧性与可持续性挑战的量子计算
Quantum Computing for Climate Resilience and Sustainability Challenges
AI总结：
本综述探讨了量子计算与量子机器学习在气候变化预测及可持续发展中的应用，强调其在优化多基础设施系统以实现气候中性方面的潜力，并评估了当前量子算法与硬件在废物转化能源、洪水预测和碳捕获材料开发等实际案例中的性能。
摘要：
气候变化影响的不断加剧，以及对可持续发展和自然资源管理日益增长的需求，迫切需要创新性的技术解决方案。量子计算（QC）已成为一种极具前景的工具，有望彻底改变这些关键领域。本综述探讨了量子机器学习与优化技术在气候变化预测及促进可持续发展中的应用。传统计算方法在处理气候模型与自然资源管理的规模和复杂性时往往力不从心。然而，量子的进步在计算效率和问题解决能力上提供了显著提升。通过综合最新研究与发展，本文强调了QC与量子机器学习如何优化多基础设施系统以实现气候中性。本文还评估了当前量子算法与硬件在实际应用中的性能，并展示了现实案例，即厌氧消化中的废物转化能源、洪水预测中的灾害预防，以及用于碳捕获的新材料开发。这些量子技术的整合有望推动实现气候韧性与可持续发展的重大进步。
英文摘要：
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
