Multiple sources of pollution, nonlinear interactions, and competing environmental and economic goals are characteristics of complicated optimisation problems in urban air pollution control. When dealing with such high-dimensional decision spaces, conventional optimisation strategies frequently encounter scaling issues. This study suggests an optimisation structure for urban air pollution management methods based on quantum computing. In order to minimise pollutant concentrations while meeting operational and regulatory restrictions, the suggested method formulates air pollution reduction as a constrained optimisation problem in which industry rules, traffic management policies, and emission reduction strategies are optimised. To effectively explore the solution space, a hybrid quantum–classical optimisation model is created employing quantum-inspired algorithms. The framework's efficiency is compared with traditional optimisation techniques with respect to of convergence effectiveness and solution quality using simulated municipal air quality & emission data. The results show that for complicated pollution management scenarios, the quantum-based optimisation strategy achieves better optimisation performance and faster convergence. The results show how quantum computing paradigms can be a useful tool for solving large-scale environmental optimisation issues and assisting with data-driven the air quality in cities management.
Multiple sources of pollution, nonlinear interactions, and competing environmental and economic goals are characteristics of complicated optimisation problems in urban air pollution control. When dealing with such high-dimensional decision spaces, conventional optimisation strategies frequently encounter scaling issues. This study suggests an optimisation structure for urban air pollution management methods based on quantum computing. In order to minimise pollutant concentrations while meeting operational and regulatory restrictions, the suggested method formulates air pollution reduction as a constrained optimisation problem in which industry rules, traffic management policies, and emission reduction strategies are optimised. To effectively explore the solution space, a hybrid quantum–classical optimisation model is created employing quantum-inspired algorithms. The framework's efficiency is compared with traditional optimisation techniques with respect to of convergence effectiveness and solution quality using simulated municipal air quality & emission data. The results show that for complicated pollution management scenarios, the quantum-based optimisation strategy achieves better optimisation performance and faster convergence. The results show how quantum computing paradigms can be a useful tool for solving large-scale environmental optimisation issues and assisting with data-driven the air quality in cities management.
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.
Optimization problems that are computationally intractable can be tackled with the help of AI and quantum computing. Standard optimization techniques are ill-equipped to deal with high-dimensional search spaces and non-linear constraints in domains such as logistics, finance, energy management, pharmaceutical research, and large-scale industrial scheduling. Using quantum computing with AI-driven learning, this research presents a hybrid optimization strategy that enhances the efficiency and accuracy of problem-solving. Adaptive optimization, parameter changes, and pattern discovery are all part of machine learning models, whereas quantum annealing and variational quantum eigen solvers simultaneously explore massive solution spaces. The proposed method uses quantum probabilistic computation and heuristic refining based on AI to reduce computational complexity and speed up the convergence of global optimums. While quantum computers optimize computationally expensively at the quantum-classical interface, deep learning models preprocess and extract features. Compared to metaheuristics, simulation-based performance analysis improves both the quality of solutions and the speed of convergence. The study demonstrated that AI frameworks boosted with quantum computing may solve combinatorial optimization problems and NP-hard problems. Using a computational paradigm that is both scalable and future-proof, this work reimagines optimization in the scientific, industrial, and engineering spheres.
In this paper, Pontryagin’s Maximum Principle is used to develop a mathematical model for optimal pollution control. It includes the dynamics of spatial pollution distribution, natural decay, nonlinear saturation, and economic mechanisms for emission regulation. The numerical solution is based on a quantum-classical approach that uses finite difference methods to model pollution dynamics and the Variational Quantum Algorithm to determine optimal pollution control strategies. The optimal control distributions, the evolution of the adjoint variables, and heatmaps of pollutant dispersion, as well as pollution dynamics, are shown for the results. These findings can be used to design environmentally sustainable pollution management strategies that support economic development and environmental protection.
Air pollution forecasting is a critical challenge in environmental science. Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are two of the most harmful gases, contributing significantly to respiratory illnesses and environmental degradation. Accurate predictions of NO2and CO levels help mitigate the adverse effects of pollutants on public health. A hybrid quantum-classical machine learning model is developed, integrating a feature map, variational ansatz, and quantum regressor optimized with multiple optimizers. The results are compared with classical machine learning model, such as Multi-Layer Perceptron (MLP), demonstrating the potential of quantum computing in predictive modeling. Experimental results using the air quality dataset reveal that the proposed model achieves superior performance compared to traditional approaches, as measured by MSE, RMSE, MAE, and R2. The findings suggest that quantum-enhanced neural networks provide a promising avenue for real-time air pollution forecasting, enabling proactive pollution control measures.
Optimization problems are widely encountered in real-world domains such as energy systems, financial markets, transportation networks, supply chain logistics, manufacturing processes, and e-commerce platforms. Traditional classical computing methods often struggle to solve large combinatorial optimization problems due to the exponential growth of the solution space. Quantum computing has emerged as a promising approach to address these challenges by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum hardware is limited by noise, restricted qubit availability, and scalability issues, making fully quantum solutions impractical for large-scale applications. To overcome these limitations, this research proposes a hybrid quantum–classical supercomputing framework for solving complex optimization problems across multiple domains. The framework integrates classical data processing and optimization techniques with quantum-inspired algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), to efficiently explore large solution spaces. Real-world datasets from energy systems, supply chain logistics, financial markets, transportation networks, manufacturing processes, and e-commerce platforms are used for experimental evaluation. The results demonstrate that the hybrid optimization approach can generate higher-quality solutions and improved optimization accuracy compared to traditional classical methods. These findings highlight the potential of hybrid quantum–classical systems as an effective and scalable solution for solving complex optimization problems in modern computational applications.
Recently, quantum computing has gained attention in urban studies as a tool for complex transport planning problems, but its role remains unclear. This paper reviews quantum computing research in urban transport planning and highlights major limits in scalability, robustness, constraint handling, and engineering feasibility.Stable and reproducible advantages of quantum optimisation in real urban systems have yet to be shown. By comparing quantum methods with established classical optimisation methods, it is found that decomposition methods, metaheuristics, and reinforcement learning already provide transparent, scalable, and policy-interpretable solutions for medium and large-sized urban transport networks. In contrast, the contribution of quantum methods largely lies in the exploratory analysis of limited, discrete combinatorial subproblems rather than full system-level optimisation. It is argued in this paper for a shift from technology-driven application narrative towards problem-driven method selection. From an urban transport planning perspective, we have identified the specific problem types where the exploratory use of quantum computing may be relevant, including critical link and node vulnerability identification, combinatorial screening of congestion and failure scenarios, disaster-related condition analysis, constrained path option selection, and small-scale facility location and investment option assessment. It is concluded that hybrid frameworks represent a more realistic pathway for integrating quantum computing into urban transport research, in which classical methods ensure systemlevel consistency and policy interpretability while quantum methods support local combinatorial exploration. Until stable engineering advantages are demonstrated, public agencies and researchers should prioritise method validation, scenario suitability, and cross-disciplinary collaboration.