The greenhouse gas (GHG) emissions issue that is directly related to the 13th Sustainable Development Goals ; Climate Action has gained attention on a global scale, prompting the utilization of all available technological advancements, including quantum computing . This systematic literature review, employing Kitchenham’s method, explores the realm of quantum computing and its application to the pressing issue of GHG emissions. Through a meticulous analysis of scholarly articles, we identify key trends, influential authors, core sources, and relevant affiliations within this research domain. Notably, our findings underscore a robust connection between quantum computing studies and the fields of machine learning and optimization, where various optimization tasks attempt to minimize GHG emissions, predominantly in the Energy and Logistics problem domain using Quantum-inspired Evolutionary Algorithm , Quantum-inspired Swarm Optimization, or Quantum Annealing. An insightful map reveals the emergence of diverse quantum computing implementations for varied tasks, across various domains, providing nuanced perspectives and identifying potential research directions, particularly in optimization and prediction tasks. This study offers a foundational understanding of trends, challenges, and opportunities associated with quantum computing implementation in addressing GHG emissions, contributing to the ongoing establishment of sustainable technology.
The exponential growth of urban populations has necessitated the development of advanced energy models to manage resources efficiently and mitigate carbon emissions. Traditional approaches to urban energy modeling are hampered by computing limitations, primarily due to the computational power required and the inefficiencies of classical algorithms when handling high-dimensional search spaces.This paper explores the application of Quantum Approximate Optimization Algorithm (QAOA) to solve large-scale urban energy models, presenting a novel approach that leverages the computational advantages of quantum computing. QAOA offers several key benefits in this context, including enhanced capability to navigate and optimize within complex, high-dimensional search spaces. Its hybrid quantum-classical nature allows for significant improvements in computational efficiency and solution accuracy. Additionally, QAOA provides robustness against local minima, a common challenge in classical optimization techniques, thereby yielding more optimal solutions for energy distribution and management.By addressing the current constraints faced by classical computers, such as their inability to efficiently process the vast and complex data inherent in urban energy systems, this study demonstrates the potential for quantum algorithms to revolutionize urban planning and smart city development. The research highlights key areas where QAOA can outperform classical methods, offering enhanced accuracy and speed in optimizing energy distribution, reducing overall carbon emissions, and contributing to more sustainable urban environments. Our findings indicate that quantum computing holds significant promise in overcoming the existing barriers, providing a robust framework for the future of urban energy management.
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.
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.
Rapid urbanization and climate change demands require transformative strategies for energy management in smart cities. Traditional computational techniques encounter substantial difficulties in optimizing intricate, dynamic, and large-scale energy resource allocation issues characteristic of contemporary smart grids. This conceptual review examines the emerging paradigm of quantum-enhanced resource allocation strategies for sustainable energy management in smart cities. This paper expands on the foundational framework of quantum computing applications for smart grid digital twins proposed by Lemo et al., synthesizing current research, developing an integrated conceptual model, and identifying critical research trajectories. We systematically analyze how quantum algorithms—specifically Quantum Annealing (QA), the Quantum Approximate Optimization Algorithm (QAOA), and the Variational Quantum Eigensolver (VQE)—can tackle significant optimization challenges in energy distribution, demand-response management, and renewable energy integration. Our analysis highlights the intersection of quantum computing and digital twin technology as a crucial facilitator for real-time, adaptive energy management systems. We propose a five-layer Quantum-Digital Twin (Q-DT) integration framework and examine its implications for sustainable urban development. The paper concludes by identifying critical research deficiencies, such as the necessity for standardized problem mapping, empirical benchmarking studies, and ethical governance frameworks, while delineating a prospective research agenda for achieving quantum advantage in smart city energy ecosystems. Keywords: Quantum Computing, Resource Allocation, Intelligent Urban Environments, Sustainable Energy Management, Digital Twins, Quantum Optimization, Smart Grid, Integration of Quantum and Digital Twins Aims Research Journal Reference Format: Ojeniyi, J. A., Fasola, O. O., Onyeabor, G. A., Mohammed, A. A, Aliyu, A. A., Ahmed, H. M. & Ndanusah, U. H. (2026): A Conceptual Review of Quantum-Enhanced Resource Allocation Strategies for Sustainable Energy Management in Smart Cities. Advances in Multidisciplinary Research Journal. Vol. 12 No. 1, Pp 55-61. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V12N1P4
The rapid growth of data-intensive applications and cloud computing has significantly increased the energy consumption of modern computing infrastructures, particularly data centers, raising critical sustainability and environmental concerns. Green computing seeks to address these challenges by improving energy efficiency and reducing carbon footprints without sacrificing performance. Quantum computing has emerged as a promising paradigm due to its ability to exploit quantum mechanical principles such as superposition and entanglement to achieve computational speedups beyond classical limits. This paper examines the role of quantum computing in advancing green computing technologies by analyzing its algorithmic capabilities, application domains, and hardware-related energy implications. Key quantum algorithms, including Shor’s algorithm, Grover’s search, the Quantum Approximate Optimization Algorithm (QAOA), and quantum simulation methods, are reviewed with respect to their potential to reduce execution time and energy consumption for computationally intensive tasks such as cryptography, optimization, and system modeling. The study further explores sustainability-oriented applications, including energy systems optimization, materials discovery for renewable technologies, and quantum machine learning for resource forecasting and management. Comparative analysis indicates that quantum approaches may offer substantial energy-efficiency advantages over classical methods as hardware matures. However, significant challenges remain, including qubit decoherence, error correction overhead, cryogenic cooling requirements, and scalability limitations. The paper concludes that continued advances in hybrid quantum–classical architectures and fault-tolerant hardware are essential for realizing quantum computing’s potential contribution to sustainable and energy-efficient computing.