Objectives: The research aims to demonstrate the integration between Quantum Computing (QC) and Predictive Analysis (PA) and their role in reducing costs while achieving Sustainable Development Goals (SDGs). The study addresses the inefficiencies in calculating and measuring product costs under traditional systems and examines how QC and PA can enhance cost reduction and product quality to better meet customer needs. Additionally, the research seeks to strengthen the theoretical framework with practical applications, illustrating how this integration improves a company’s competitive position while promoting social, environmental, and economic sustainability. Methods: The study employs a descriptive analytical approach, focusing on the practical application of QC and PA in cost management. It relies on financial and cost data from a selected company to analyze the impact of integrating QC and PA on cost reduction and sustainable development. The methodology aims to provide empirical evidence supporting the effectiveness of this integration in optimizing resource and energy use. Results: The research findings confirm that integrating QC and PA significantly contributes to cost reduction, which in turn plays a crucial role in achieving SDGs. The study highlights how this integration enhances efficiency in resource and energy utilization, identifies weaknesses in traditional cost systems, and supports the transition toward sustainable manufacturing. By improving production processes and minimizing waste, the application of QC and PA fosters innovation in sustainable product development. Conclusion: The study underscores the importance of integrating QC and PA in modern cost management strategies to achieve sustainability objectives. Encouraging innovative local manufacturing through enhanced efficiency and resource optimization supports the transition toward sustainable production. The findings suggest that companies adopting QC and PA can improve their competitive edge, reduce costs, and contribute to environmental and economic sustainability, ultimately aligning with global sustainable development goals.
Objectives: The research aims to demonstrate the integration between Quantum Computing (QC) and Predictive Analysis (PA) and their role in reducing costs while achieving Sustainable Development Goals (SDGs). The study addresses the inefficiencies in calculating and measuring product costs under traditional systems and examines how QC and PA can enhance cost reduction and product quality to better meet customer needs. Additionally, the research seeks to strengthen the theoretical framework with practical applications, illustrating how this integration improves a company’s competitive position while promoting social, environmental, and economic sustainability. Methods: The study employs a descriptive analytical approach, focusing on the practical application of QC and PA in cost management. It relies on financial and cost data from a selected company to analyze the impact of integrating QC and PA on cost reduction and sustainable development. The methodology aims to provide empirical evidence supporting the effectiveness of this integration in optimizing resource and energy use. Results: The research findings confirm that integrating QC and PA significantly contributes to cost reduction, which in turn plays a crucial role in achieving SDGs. The study highlights how this integration enhances efficiency in resource and energy utilization, identifies weaknesses in traditional cost systems, and supports the transition toward sustainable manufacturing. By improving production processes and minimizing waste, the application of QC and PA fosters innovation in sustainable product development. Conclusion: The study underscores the importance of integrating QC and PA in modern cost management strategies to achieve sustainability objectives. Encouraging innovative local manufacturing through enhanced efficiency and resource optimization supports the transition toward sustainable production. The findings suggest that companies adopting QC and PA can improve their competitive edge, reduce costs, and contribute to environmental and economic sustainability, ultimately aligning with global sustainable development goals.
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.
As quantum computing progresses from theory to practical systems, its environmental and economic sustainability remains underexplored.This paper analyzes both the carbon footprint and cost implications of quantum computing technologies, with attention to their alignment with global sustainability goals.Key contributors to energy consumption, emissions, and operational costs are identified across the quantum computing lifecycle: hardware manufacturing, cryogenic cooling, runtime power demands, and quantum circuit simulation.The study employs a qualitative methodology, integrating a comprehensive review of scientific literature, environmental assessments, cost analysis reports, and benchmarking frameworks.It further presents a comparative analysis of quantum and classical high-performance computing (HPC), evaluating energy efficiency, environmental impact, and cost-effectiveness across realistic scenarios.By addressing both sustainability and economic dimensions, this research provides new insights for developers and policymakers, supporting the advancement of greener and more costeffective quantum technologies.
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.
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 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.
This research investigates the potential of quantum computing in production planning and addresses the limitations of conventional computing approaches. Traditional methods have been partially effective, but they struggle to solve complex optimization problems, accurately predict demand, and manage supply chains efficiently. The unique computational capabilities of quantum computing offer promising solutions to surmount these obstacles and revolutionize production planning processes. This study seeks to bridge the gap between quantum computing and production planning by analyzing the benefits, limitations, and challenges of its applicability in this field. It proposes customized algorithms and methodologies for leveraging quantum computation to enhance production planning efficiency, cost reduction, and decision-making processes. The research demonstrates the potential of quantum algorithms to minimize total production costs while appeasing demand and resource constraints through a numerical example and mathematical formulation. The results emphasize the advantages of quantum computing in terms of cost reduction, enhanced efficiency, and scalability. Comparisons with conventional methods illuminate the benefits and drawbacks of quantum computing in production planning. This research contributes to the development of novel strategies to improve production planning efficiency, lower costs, and enhance decision-making processes, allowing organizations to leverage quantum computing for optimized production operations