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.
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.
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 influence of quantum computing techniques on the optimization of resource allocation in the agricultural sector is analyzed. The use of variational quantum eigensolver (VQE) algorithm to achieve optimal resource allocation is explored. It has been found that production functions, such as the Cobb-Douglas function, effectively describe the transformation of input parameters into output. Parameterization of a quantum circuit and minimization of the expected value of the Hamiltonian to optimize resource allocation are studied. It is determined that the quantum optimization approach demonstrates improved computational efficiency compared to classical methods. It has been found that the integration of quantum technologies can significantly improve resource allocation in agriculture. An assumption has been made about increasing productivity and sustainability in the agricultural sector thanks to quantum methods. Further research on quantum algorithms is proposed to improve the efficiency of natural resource management. New software has been developed for modeling and optimizing production functions in agriculture. The use of quantum computing to solve complex problems in the agricultural sector is justified. A detailed description of the quantum algorithm and its benefits in the context of agriculture is presented.
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.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.
Bioprocess optimization spans multiple scales, from molecular interactions to plant-wide operations, presenting challenges due to nonlinearity, high-dimensionality, and computational intractability. Traditional methods, such as Mixed-Integer Nonlinear Programming (MINLP), often fail to efficiently solve these problems. This work explores Hybrid Quantum-Classical (HQC) algorithms as a promising alternative. By integrating quantum solvers for discrete combinatorial optimization with classical solvers for continuous variable refinement, HQC approaches overcome computational bottlenecks inherent to classical techniques. The application of HQC algorithms enhances efficiency, scalability, and decision-making in bioprocess optimization, particularly in metabolic pathway selection, real-time fermentor control, and plant-wide resource allocation. This perspective outlines the theoretical foundation of HQC methods, their applications across bioprocess scales, and future research directions.