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.
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.
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.
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.
Global challenge of climate change demands precise predictive models to grasp its effects and create sensible mitigating plans. Based on classical computing methods, traditional models suffer in processing complicated climate data, which results in lower accuracy in long-term forecasts and extreme weather prediction. With its capacity to do huge parallel computations via superposition and entanglement, quantum computing offers a potential fix for improving climate models. In addition to focused on its ability to increase weather forecasting accuracy, long-term climate projections, and extreme weather event predictions, this work investigates the integration of quantum computing in climate science. Quantum-assisted climate models demonstrate better computational efficiency, higher forecast accuracy, and better resource management for mitigating techniques including sustainable agriculture, carbon capture, and solar energy deployment. However, obstacles, developments in quantum technology and multidisciplinary cooperation between climate researchers and quantum physicists are opening the path for more dependable and scalable climate solutions.
Climate change is accelerating, necessitating sophisticated forecasting techniques to alleviate its impacts efficiently. The huge amount and diversity of climate data frequently exceed the endurance of traditional computing techniques, creating problems with accuracy and processing speed. This research presents a quantum-enhanced framework for big data analytics in climate science by combining quantum machine learning, optimisation, and simulation methods. The recommended strategy empowers specific analysis of high-dimensional climate datasets, contributing noteworthy enhancements in predicting critical phenomena such as temperature irregularities, rainfall trends, and catastrophic weather patterns. By including quantum algorithms, the framework attains scalability and real-time competencies, minimising computational processing times while improving predictive accuracy. These results demonstrate the advantage of quantum-enhanced models over classical approaches by giving innovative tools for policymakers and industry leaders to help them make data-driven, well-informed decisions. The study highlights the revolutionary potential of quantum computing in addressing global climate challenges, making new paths for environmentally friendly remedies. As quantum technologies develop, their use in climate science will motivate innovative solutions for the endurance and preservation of the environment. Major Findings: This study highlights the necessity for cross-disciplinary collaboration to completely integrate quantum computing advancements into climate modelling, guaranteeing actionable and accurate perceptions. The results show how important quantum-enhanced analytics can be in helping to develop sustainable decision-making to deal with the escalating climate crisis.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.
Since computers were invented, the technology of computers has advanced every year. Recently, quantum computing and artificial intelligence have played an important role in computer technology, which has provided an effective platform to address challenges such as climate change and carbon emissions. The main goal of this paper is to demonstrate how the integration of quantum computing and artificial intelligence can transform climate science, with an emphasis on predicting and managing carbon emissions. In this paper, we started with introduction about climate change and technology in combating environmental problems. Then, we moved on to an introduction to quantum computing and its basics. During our research, the benefits of quantum computing over classical systems have been identified with specific reference to climate modeling and handling large data sets. We explained the role of artificial intelligence in climate science with existing AI methods in climate modeling and the gaps that they have. We evaluated the combination of quantum computing and AI to reveal the possibility of future advancements in processing time, prediction, and efficiency. We brought up some AI concerns about its policy-making capacity and carbon altering the conventional approaches to decision-making to foster sustainability. Moreover, our work is supported by real-life examples and cases of how quantum AI is superior to traditional models in estimating carbon emissions. Finally, we specified some future directions for quantum AI, including technological developments, ethical issues, and sustainability, as well as suggestions for researchers and policymakers on how to use quantum AI for solving environmental problems.