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.
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.
In this paper, a quantum-classical hybrid optimization method is developed to maximize the biomass of several plant species and ensure the efficient use of available resources. The developed model consists of two parts: the first part uses a classical mathematical model and the least squares method to determine plant growth parameters based on accurate field data; the second part uses a quantum variational quantum algorithm to optimally allocate resources. The resource allocation determined by the quantum algorithm is integrated into optimal control functions based on Pontryagin theory. The model results reflect the time-dependent dynamics of biomass for different plant species, control functions, and the influence of soil conditions (electrical conductivity and pH). The results obtained show that the quantum-classical hybrid approach provides an increase in plant biomass and efficient use of resources, and also accurately takes into account the influence of soil conditions on the growth process.
The integration of quantum optimization techniques into chemical engineering has emerged as a frontier in achieving more efficient, sustainable, and adaptive process design. This study presents a qualitative investigation into the role of multi-objective quantum optimization frameworks for enhancing process intensification in next-generation chemical plants. The research explores how quantum-inspired algorithms—leveraging quantum superposition, entanglement, and probabilistic search mechanisms—can optimize multiple competing objectives such as energy consumption, production throughput, and environmental impact simultaneously. Through a comprehensive review of both academic and industrial literature, the study identifies key trends in the use of hybrid quantum–classical models for solving high-dimensional optimization problems in chemical plant design and operation. It examines how quantum-assisted methods, including the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), can accelerate simulation convergence and decision-making in process systems engineering. The qualitative findings underscore the transformative potential of quantum-enhanced computation in enabling process intensification, where chemical operations are redesigned for maximum efficiency and minimal ecological footprint. Furthermore, this research analyzes the interdisciplinary interface between quantum computing, chemical engineering, and sustainable manufacturing, emphasizing how algorithmic innovation can support cleaner, more resource-efficient production ecosystems. Conceptual models were developed to illustrate the role of quantum optimization in guiding strategic decisions related to resource allocation, system design, and environmental trade-offs. The study concludes that quantum optimization will likely become a cornerstone of the digital and sustainable transformation of the chemical industry, facilitating intelligent automation and data-driven sustainability in the decades ahead.
In this paper, a quantum-classical hybrid optimization method is developed to maximize the biomass of several plant species and ensure the efficient use of available resources. The developed model consists of two parts: the first part uses a classical mathematical model and the least squares method to determine plant growth parameters based on accurate field data; the second part uses a quantum variational quantum algorithm to optimally allocate resources. The resource allocation determined by the quantum algorithm is integrated into optimal control functions based on Pontryagin theory. The model results reflect the time-dependent dynamics of biomass for different plant species, control functions, and the influence of soil conditions (electrical conductivity and pH). The results obtained show that the quantum-classical hybrid approach provides an increase in plant biomass and efficient use of resources, and also accurately takes into account the influence of soil conditions on the growth process.
The article considers the issue of scientifically based formation and management of the population of plants that restore the natural landscape, such as saxaul, yulgin, kandim and cherkes, in saline and severely degraded desert areas. These plants make a significant contribution to reducing wind erosion in the region, reducing soil salinity, restoring ecological stability and strengthening the feed base for livestock. The study proposes a hybrid mathematical model that describes their biomass growth, spatial distribution, competition and response to management influences. A computational approach based on a variational quantum algorithm is used to determine the central control parameter of the model. In this case, the optimal control value is found by minimizing the smallest value of the Hamiltonian energy. The spatial-temporal dynamics of plants is assessed through surface distribution maps of biomass, ecological stress indices, competition indicators, management costs and Hamiltonian evolution. The results have important practical significance in planning desert plant plantings, creating ecologically sustainable landscapes, and establishing sustainable agroecological systems in saline areas.
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 conventional plant breeding has had some limitation in addressing the issues of global food security and challenges. Quantum computing proposes the way of improvement of genetic algorithm based optimization in plant breeding. The study attempts to analyse the impact of quantum computing in the acceleration of plant breeding. The chapter likewise examines the use of quantum algorithms and methods to anticipate complicated traits utilising genome selection and trait mapping. The research also focuses on quantum computing in order to find better gene combination, optimization-based breeding and crop resiliency in the face of the climate changes. Certain areas specifically include quantum acceleration, genome-wide association studies (GWAS) and quantum machine learning for phenotyping prediction and quantum inspired optimization algorithms to design novel breeding schemes. Such perspective paves way for new era to concentrate on improvement of crop and sustainable food production. Such innovation offers something to solve the global food security challenge in the 21st century.