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IEEE International Conference on Renewable Energy and Sustainable E-Mobility (RESEM)

Quantum Computing in Smart Energy Management: A Novel Algorithmic Framework for Efficient Energy Optimization in Smart Cities

Rohan Verma, Prabh Deep Singh, Kiran Deep Singh

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/resem70152.2026.11721745

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Abstract: The integration of Quantum Artificial Intelligence (Quantum AI) into smart cities represents a transformative leap in traffic management and urban planning. Traditional AI-driven urban systems, while effective, struggle with real-time adaptability, large-scale data processing, and complex optimization challenges. Quantum AI, leveraging quantum superposition, entanglement, and parallel computation, enables cities to optimize traffic flow, infrastructure planning, energy management, and public safety with unparalleled speed and accuracy. This chapter explores how Quantum AI enhances predictive traffic modeling, real-time traffic light synchronization, and autonomous vehicle coordination, reducing congestion and improving mobility efficiency. Additionally, Quantum AI is reshaping urban planning by enabling smart infrastructure development, sustainable resource management, and proactive disaster response systems. Despite challenges such as high computational costs, data privacy concerns, and ethical implications, Quantum AI holds the potential to revolutionize city management, paving the way for resilient, sustainable, and highly adaptive urban environments. The future of smart cities will be defined by AI-powered quantum ecosystems, ensuring seamless urban mobility, enhanced sustainability, and intelligent decision-making for next-generation urban development. Keywords: Quantum AI, smart cities, traffic optimization, urban planning, quantum computing, real-time traffic management, predictive analytics, sustainable urban development, artificial intelligence, autonomous transportation, disaster response, smart infrastructure, quantum-enhanced mobility.
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.