Quantum approximate optimisation algorithm (QAOA) for surplus energy distribution in urban microgrids

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.