This paper studies the use of quantum computing to disrupt serious problems in a sustainable development space. In our view, the quantum algorithms can disrupt studies related to carbon capture, renewable energy optimization and sustainable agriculture (such as Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm). We demonstrate that quantum after a broad underlining of existing studies and theoretical avenues has the potential to have practical, even expedited pathways for attaining multiple goals on United Nations Sustainable Development Goals list. It covers the mathematics behind these quantum algorithms, and discusses case studies that investigate their applications as well as ethical consideration and future directions. The case for quantum computing as a technology solution to our sustainable future: An evidence-based perspective is expected to be strong.
This paper studies the use of quantum computing to disrupt serious problems in a sustainable development space. In our view, the quantum algorithms can disrupt studies related to carbon capture, renewable energy optimization and sustainable agriculture (such as Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm). We demonstrate that quantum after a broad underlining of existing studies and theoretical avenues has the potential to have practical, even expedited pathways for attaining multiple goals on United Nations Sustainable Development Goals list. It covers the mathematics behind these quantum algorithms, and discusses case studies that investigate their applications as well as ethical consideration and future directions. The case for quantum computing as a technology solution to our sustainable future: An evidence-based perspective is expected to be strong.
This paper studies the use of quantum computing to disrupt serious problems in a sustainable development space. In our view, the quantum algorithms can disrupt studies related to carbon capture, renewable energy optimization and sustainable agriculture (such as Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm). We demonstrate that quantum after a broad underlining of existing studies and theoretical avenues has the potential to have practical, even expedited pathways for attaining multiple goals on United Nations Sustainable Development Goals list. It covers the mathematics behind these quantum algorithms, and discusses case studies that investigate their applications as well as ethical consideration and future directions. The case for quantum computing as a technology solution to our sustainable future: An evidence-based perspective is expected to be strong.
The rapid advancements in quantum computing hold transformative potential for addressing critical global challenges and achieving sustainable development. This paper presents a conceptual framework that aligns quantum computing innovations with the United Nations Sustainable Development Goals (SDGs). It explores key areas such as renewable energy optimization, smart city infrastructure, and climate change mitigation. The study also emphasizes sustainability challenges, including environmental impacts, ethical concerns, and socio-economic considerations like workforce transitions and digital equity. A novel Carbon-aware Quantum Computing model is introduced to minimize the environmental footprint of quantum systems. Finally, the paper proposes a roadmap for ethical and scalable adoption of quantum technologies, ensuring inclusive and resilient societal growth.
Sustainable Development Goal 7 (SDG-7) emphasises universal access to affordable, reliable, and modern energy by 2030. Achieving this target requires transformative energy production, distribution, and financial optimisation solutions. This paper investigates the role of quantum computing in advancing SDG-7 objectives, focusing on quantum optimisation algorithms, simulations, and machine learning techniques. By analyzing global and national energy progress indicators, particularly from India, the study identifies areas where quantum algorithms such as Quantum Annealing (QA), Adiabatic Algorithms (AA), QAOA, and Quantum Machine Learning (QML) offer measurable impact in intelligent energy grid optimization, clean energy forecasting, and resource-efficient investment modeling. Further, the paper assesses financial pathways enabled by quantum-enhanced models to support renewable energy rollouts. The results underline quantum computing’s potential to accelerate access, reduce emissions, and maximise economic efficiency in energy systems aligned with SDG-7. This study offers a novel contribution by linking quantum algorithm performance to both energy optimisation and financial outcomes aligned with SDG-7.
Quantum computing offers unprecedented opportunities to revolutionize equitable green innovation by addressing complex sustainability challenges with enhanced computational power. This chapter explores how quantum algorithms can optimize resource management, improve energy efficiency, and accelerate the adoption of green technologies. By leveraging quantum-based predictive models and optimization techniques, industries can achieve sustainable practices while ensuring equity in resource distribution and access. The chapter also delves into the potential of quantum computing to tackle intricate problems in supply chain sustainability, renewable energy integration, and environmental impact analysis. Challenges such as scalability, accessibility, and ethical considerations are addressed, alongside proposed solutions to overcome these barriers. Real-world use cases and emerging research highlight the transformative potential of quantum computing in fostering a sustainable and inclusive future, bridging the gap between innovation and equity.
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.
Quantum computing (QC) offers a pathway to solving certain problems that classical computers struggle with, such as advanced drug discovery, secure cryptography, and the design of new materials. While these capabilities are promising, the field also raises serious sustainability concerns. Running quantum processors demands significant amounts of energy, rare elements, and specialized infrastructure, often without clear guidelines for environmentally responsible design. This paper reviews the environmental impact of QC by examining inefficiencies in energy use, cooling, and hardware manufacturing. The findings indicate that integrating renewable energy sources, adopting energy-efficient quantum hardware designs, and developing optimized, low-power quantum algorithms are the most effective strategies for reducing QC’s environmental footprint. In addition, a set of potential sustainability metrics is proposed to guide future research and implementation. The discussion points to one clear message: if sustainability is not embedded into quantum technology now, we risk repeating the environmental mistakes made in past computing revolutions.