The article considers the solution of the optimal control tasks in ecology and environmental protection based on quantum computing. Pollution dynamics are investigated, with natural decay, diffusion and controlled remediation treatments being considered. Optimal control is designed to minimize environmental impact while also minimizing the costs of cleaning up the environment. To solve the problem effectively, a quantum optimization algorithm is proposed. For this, the desired problem is transformed into a quadratic optimization problem. This enables the application of quantum computing to the formulation of optimal policies for environmental regulation. Experiments are performed, a comparison is made between quantum-classical approaches and conventional techniques, and the feasibility of quantum optimization in sustainability issues is debated.
The article considers the solution of the optimal control tasks in ecology and environmental protection based on quantum computing. Pollution dynamics are investigated, with natural decay, diffusion and controlled remediation treatments being considered. Optimal control is designed to minimize environmental impact while also minimizing the costs of cleaning up the environment. To solve the problem effectively, a quantum optimization algorithm is proposed. For this, the desired problem is transformed into a quadratic optimization problem. This enables the application of quantum computing to the formulation of optimal policies for environmental regulation. Experiments are performed, a comparison is made between quantum-classical approaches and conventional techniques, and the feasibility of quantum optimization in sustainability issues is debated.
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.
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.
Pollution and global warming become more and more of a threat recently, so creative solutions are required to tackle their overwhelming complexity, surpassing the limitations of traditional computational methods. Using the concepts of quantum physics, quantum computing presents a revolutionary approach for solving such environmental problems. With the help of a variety of data in usage from reputable global scientific sources, including world databases, pollution monitoring networks, and climate models in addition, the current scientific paper explores the quickly developing potential of quantum computing to reduce pollution and global warming. The complexities of quantum algorithms are object of our exploration, focusing on those that have relevance in resource management, in order to shed light on how quantum computers might transform decision-making processes toward global environmental sustainability. Techniques for quantum optimization show promise in maximizing energy grid distribution and reducing waste generation in complex supply chains.
Quantum computing leverages the principles of quantum mechanics to process information in ways that classical computers cannot. This capability is particularly advantageous for solving complex optimization problems that are common in supply chain management. Quantum algorithms, such as the quantum approximate optimization algorithm (QAOA) and quantum annealing, have shown promise in efficiently solving these problems by exploring numerous potential solutions simultaneously and identifying optimal strategies. The purpose of this chapter is to investigate the rapidly developing topic of quantum computing and its potential applications in managing sustainable resources within supply chains. Traditional resource allocation methods often struggle to maximize efficiency while minimizing environmental impact. However, new developments in quantum computing have opened up potentially fruitful pathways for addressing these issues. This study aims to explore how quantum computing can revolutionize through an examination of quantum algorithms, optimization approaches, and case studies.
Plant biodiversity plays an important role in the sustainability of ecosystems and human societies. The resilience of plants in the context of biodiversity relates to their ability to adapt to changing environmental conditions, provide ecosystem services and meet human needs. This article examines the influence of quantum variational optimization on the solution of a system of differential equations for a model of plant biodiversity and sustainability, taking into account the interaction between two populations. The system of equations models the dynamics of changes in the density of plant populations of types and over time, consider the influence of interaction, growth coefficients and the intensity of the impact of diseases and pests. The numerical integration method is used to solve the system of equations, and quantum variational optimization is also introduced. Quantum variational optimization is performed with the goal of minimizing the error obtained from a quantum computational experiment. An analysis is made of the optimal parameters found using quantum optimization and their impact on population dynamics. The article provides a comprehensive approach to studying the influence of quantum variational optimization on the solution of differential equations, and discusses the potential prospects for using this method in environmental and biological models.
Quantum computing (QC) offers a pathway to solving certain problems that classical computers struggle with, such as advanced drug discovery, secure cryptography, and the design of new materials. While these capabilities are promising, the field also raises serious sustainability concerns. Running quantum processors demands significant amounts of energy, rare elements, and specialized infrastructure, often without clear guidelines for environmentally responsible design. This paper reviews the environmental impact of QC by examining inefficiencies in energy use, cooling, and hardware manufacturing. The findings indicate that integrating renewable energy sources, adopting energy-efficient quantum hardware designs, and developing optimized, low-power quantum algorithms are the most effective strategies for reducing QC’s environmental footprint. In addition, a set of potential sustainability metrics is proposed to guide future research and implementation. The discussion points to one clear message: if sustainability is not embedded into quantum technology now, we risk repeating the environmental mistakes made in past computing revolutions.