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International Electronic Conference on Applied Sciences (ASEC)

Application of Quantum Computing Algorithms in the Synthesis of Control Systems for Dynamic Objects

Noilakhon Sobirjоnovna Yakubova, Komil Isroilovich Usmanov, Zafar Turakulov, Jaloliddin Abdurazzakovich Eshbobaev

Published May 20, 2025• 2 citations
Publisher

Abstract

Currently, the main focus in the automation of technological processes is on developing control systems that enhance the quality of the control process. Because the systems being controlled are often complex, multidimensional, and nonlinear, quantum computing algorithms offer an effective solution. Although there are several intelligent control methods available to improve the quality of technological processes, each has certain drawbacks. Quantum algorithms, which rely on the principles of quantum correlation and superposition, are designed to optimize control while minimizing energy and resource consumption. This article discusses the diesel fuel hydrotreating process, a critical step in oil refining. The primary goal of hydrotreating is to enhance fuel quality by removing sulfur, nitrogen, and oxygen compounds. To accurately model this process, it is essential to consider not only the external factors affecting it but also its physical characteristics. By doing so, the mathematical model becomes more precise. Based on this approach, a quantum fuzzy control system for the diesel fuel hydrotreating process was developed using quantum algorithms. These algorithms can rapidly analyze large amounts of data and make decisions. At the same time, a computer model of a fuzzy quantum control system for the process of hydrotreating diesel fuel was constructed, and a number of computational experiments were carried out. As a result, a 1.8% reduction in energy costs for the diesel fuel hydrotreating process was achieved.

DOI
10.3390/engproc2025087068

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Currently, the main focus in the automation of technological processes is on developing control systems that enhance the quality of the control process. Because the systems being controlled are often complex, multidimensional, and nonlinear, quantum computing algorithms offer an effective solution. Although there are several intelligent control methods available to improve the quality of technological processes, each has certain drawbacks. Quantum algorithms, which rely on the principles of quantum correlation and superposition, are designed to optimize control while minimizing energy and resource consumption. This article discusses the diesel fuel hydrotreating process, a critical step in oil refining. The primary goal of hydrotreating is to enhance fuel quality by removing sulfur, nitrogen, and oxygen compounds. To accurately model this process, it is essential to consider not only the external factors affecting it but also its physical characteristics. By doing so, the mathematical model becomes more precise. Based on this approach, a quantum fuzzy control system for the diesel fuel hydrotreating process was developed using quantum algorithms. These algorithms can rapidly analyze large amounts of data and make decisions. At the same time, a computer model of a fuzzy quantum control system for the process of hydrotreating diesel fuel was constructed, and a number of computational experiments were carried out. As a result, a 1.8% reduction in energy costs for the diesel fuel hydrotreating process was achieved.
Around the world, the food industry is focusing on achieving energy and resource efficiency. One of the main challenges in the field of process automation is the creation of effective control systems using intelligent technologies to improve the quality of processes and achieve the production of high-quality products with less energy and resources. Therefore, it is necessary to work with a large amount of data. Particular attention is paid to the development of computational algorithms for automated high-speed computational analysis systems for processing this data at high speed. Therefore, the article discusses the use of quantum computing methods in controlling complex dynamic objects operating under conditions of uncertainty, taking into account the use of quantum computing methods that have high speed and efficiency.
The article analyzes the potential of quantum algorithms for optimizing cognitive decision-making systems in production processes in the energy sector. Compared with traditional algorithms, quantum algorithms allow high-speed processing of large amounts of variable data in real time and solving multiparameter problems based on an integrated approach. The study considers uncertainties and issues of increasing efficiency in the processes of production and distribution of electricity using a cognitive model. The integration of quantum algorithms into this model allows developing optimal strategies for using resources in production. A heating boiler was taken as a control object, and taking into account its multidimensionality and multi-connectedness, the problem of modeling an intelligent control system using quantum algorithms was solved.
Quantum Control and Fuzzy Logic can be integrated to provide a significant improvement in the performance and decision-making of autonomous system in dynamic environment. Quantum control takes advantage of optimization tasks solvable by quantum systems and fuzzy logic manages the uncertainty and inaccuracy of real time decision making. The paper will also explain how these technologies can be utilized together in autonomous vehicles and in the exploration of the ocean and how the combination can make it more efficient and flexible and trustworthy. This paper explores advantages, difficulties and uses of quantum control using fuzzy logic. The paper examines the main principles of both technologies, case studies of their successful implementation, and performance measures. The issues such as technical complexity, scalability, and integration with classical systems are also discussed. Findings indicate significant improvements in vehicles and submarines. With autonomous cars, the system improved route planning and traffic control, minimizing travel time and fuel consumption. It was found to be of great use in underwater exploration in submarines, improving obstacle avoidance and navigation in complex underwater conditions up to20−30%20-30 \%20−30%in efficiency. Fuzzy logic and quantum control together hold great promise for the advancement of autonomous technologies in the future, even in the face of scalability and error correction issues. The full potential of a hybrid system and quantum error correction can be realized through further research.
The article is devoted to the study of the possibility of uncertainty processing using quantum algorithms. Uncertainty processing is one of the most important tasks in artificial intelligence. The quality of knowledge used by the intelligent system depends on the solution of this problem. Domain knowledge contains uncertainty. The paper considers the possibility of uncertainty handling using quantum computing. Tensor method is used to represent uncertainty. The tensor method provides an object representation of fuzzy knowledge. This approach allows to investigate the properties of uncertainty which are not available in representing fuzzy knowledge by traditional methods such as fuzzy sets, Bayesian networks, Stanford confidence factor, neural networks. Tensor processing on a classical computer is a computationally complex task. Quantum algorithms provide exponential acceleration in solving such problems. The article discusses the possibility of using quantum algorithms to process fuzzy knowledge represented by tensors.
This research investigates the potential of quantum computing in production planning and addresses the limitations of conventional computing approaches. Traditional methods have been partially effective, but they struggle to solve complex optimization problems, accurately predict demand, and manage supply chains efficiently. The unique computational capabilities of quantum computing offer promising solutions to surmount these obstacles and revolutionize production planning processes. This study seeks to bridge the gap between quantum computing and production planning by analyzing the benefits, limitations, and challenges of its applicability in this field. It proposes customized algorithms and methodologies for leveraging quantum computation to enhance production planning efficiency, cost reduction, and decision-making processes. The research demonstrates the potential of quantum algorithms to minimize total production costs while appeasing demand and resource constraints through a numerical example and mathematical formulation. The results emphasize the advantages of quantum computing in terms of cost reduction, enhanced efficiency, and scalability. Comparisons with conventional methods illuminate the benefits and drawbacks of quantum computing in production planning. This research contributes to the development of novel strategies to improve production planning efficiency, lower costs, and enhance decision-making processes, allowing organizations to leverage quantum computing for optimized production operations
This paper considers the use of the quantum genetic algorithm for automatic selection of the optimum type and kind of correlation in the quantum structure of fuzzy inference.When solving intelligent and cognitive control tasks based on quantum soft computing and the principles of quantum deep machine learning, it is important to choose the type and kind of quantum correlation.It is an additional physical and informational computing resource in the formation of the laws of the time variation of the gains of traditional regulators located at the lower (performing) level of the intelligent control system structure.This approach is essential for the realization of adaptive and self-organizing processes of knowledge bases and guaranteed to achieve the control objectives under contingency control situations.Successful solution of the problem of choosing the type and kind of quantum correlations allows strengthening the successful search for solutions of algorithmically unsolvable problems at the classical control level.A genetic algorithm is a powerful computational intelligence toolkit for random searching of effective solutions for poorly formalized tasks.However, it has a big disadvantage when used on a classic computer: low speed and dependence on the expert's choice of a decision-making space.The paper describes the types of quantum genetic algorithms based on a combination of quantum and classical calculations, and an algorithm consisting only of quantum calculations.In such algorithm, a population can be composed of only one chromosome in a state of superposition.Immersion in the quantum structure of the fuzzy inference quantum genetic algorithm provides a synergistic effect and allows realizing quantum fuzzy inference on a classical processor.The new effect is based on the quantum genetic algorithm extracting information hidden in the classical state laws change over time the gains of traditional regulators on a new unexpected situation control.Such synergistic effect is possible only with end-to-end intellectual information technology of quantum computing and is absent at the classical level of application of the classical computing technology.