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• STUDY OF POSSIBILITIES OF USING PHOTONIC AND QUANTUM COMPUTING TECHNOLOGIES TO CALCULATE EXACT PROBABILITY DISTRIBUTIONS OF STATISTIC VALUES FROM FINITE DISCRETE SEQUENCES

  А.К. Melnikov

  121-136

  2025-12-30

  Abstract ▼

  This article explores the feasibility of using photonic and quantum computing technologies to calculate exact probability distributions of discrete sequence statistics, assuming the existence of working hardware prototypes of computing systems and the development of the required quantum algorithms. The performance evaluation of computing systems based on photonic computing technologies is based on materials from the Sarov Scientific Center for Physics and Microphysics of the Russian Academy of Sciences. The performance of a quantum computing system is assessed by comparing the time it takes to solve a boson sampling problem from a given distribution on a computing system with known performance and the time it takes to solve it on a quantum computing system. To assess the feasibility of using photonic and quantum computing technologies to calculate exact distributions, modern methods for calculating them are considered. These methods are based on solving the type multiplicity equation and a system of linear equations in non-negative integers. Analytical expressions determining the computational complexity of these methods are presented. The values of the boundaries of the parameters of exact distributions accessible for calculation using photonic and quantum computing technologies are determined. A comparison of the obtained results with the results of using multiprocessor computing technologies to calculate exact distributions using various methods is presented. An analysis of the feasibility of using photonic and quantum computing technologies to calculate exact distributions is conducted by comparing the number of parameter pairs that can be calculated for exact distributions with the total number of distribution parameters within the Fisher region, which determines a fivefold increase in sample size over the alphabet size. An analysis of the data on the number of sample parameters shows that with increasing performance of the computing technologies used, the ability to calculate exact distributions increases. However, even with the most powerful quantum technologies, this number does not exceed one-tenth of the total number of exact distributions required for statistical analysis of discrete sequences in alphabets up to 256 characters long

• QUANTUM DEEP LEARNING OF CONVOLUTIONAL NEURAL NETWORK USING VARIATIONAL QUANTUM CIRCUIT

  S.М. Gushanskiy, V. Е. Buglov

  167-177

  2021-10-05

  Abstract ▼

  Quantum computing in general and quantum deep learning represent a promising field re-lated to the research of modern methods and algorithms of quantum computing used for the pur-pose of teaching and developing new architectures of artificial neural networks. Recently, there has been a trend that research conducted in the field of quantum deep learning is becoming in-creasingly widespread among specialists. This can be explained by the fact that it has been estab-lished that quantum circuits are capable of functioning like artificial neural networks, while demonstrating the best results in solving several tasks, including, for example, the actual task of classifying objects in an image or in a video stream. Thanks to the rapid development of quantum computing in the field of deep learning, optimal solutions have been found for such urgent prob-lems as the vanishing gradient problem, finding a local minimum, improving the efficiency of large-scale parametric machine learning algorithms, eliminating decoherence and quantum er-rors, etc. Within the framework of this work, the process of functioning of a quantum variational scheme is described, its main characteristics are established, and disadvantages are identified. The key features of quantum computing, on which the process of implementing quantum deep learning with the reinforcement of a convolutional neural network is based, are also analyzed. In addition, quantum deep learning of a convolutional neural network has been carried out using a variational quantum scheme, which leads to an increase in the performance of a convolutional neural network in solving the problem of image processing, namely its classification, using a quantum computing environment. The relevance of this article consists in the implementation of a quantum deep learning algorithm with the reinforcement of a convolutional neural network for image processing, as well as the great importance of the subject of this study for the future devel-opment of quantum computing devices that can be used in artificial intelligence systems, etc., which corresponds to the priority direction of the development of domestic science

• PROSPECTS FOR THE APPLICATION OF QUANTUM COMPUTING IN ONBOARD COMPUTING SYSTEMS OF ROBOTIC COMPLEXES

  N.А. Bocharov , N.B. Paramonov

  229-239

  2025-12-30

  Abstract ▼

  Modern robotic systems are solving increasingly complex tasks, imposing higher demands on the speed and efficiency of onboard computing systems. Traditional methods of increasing performance (scaling hardware, parallel computing, etc.) are approaching their limits, necessitating the search for fundamentally new approaches. Quantum computing is considered a promising direction that could significantly surpass classical computational capabilities in certain tasks. In this regard, the goal of this study is to explore the applicability of quantum computing for onboard computing systems in robotic complexes (RCs). To achieve this goal, a comprehensive analysis of the requirements (performance, energy consumption, size and weight constraints, reliability, etc.) for onboard computing systems of RCs has been conducted. The potential of quantum algorithms in solving typical robotic tasks, including optimization problems and machine learning, has been assessed, followed by simulation modeling and comparison with classical methods. Additionally, current limitations of modern quantum computers (e.g., limited qubit count and decoherence issues) have been examined, and a forecast has been made regarding their development in the coming years based on technological trends. The study confirms the promising application of quantum computing for solving optimization and machine learning problems, which are critical for intelligent RCs. However, current technological limitations (size, operational conditions, and instability of quantum processors) do not yet allow for their direct use onboard. Nevertheless, directions for further research have been proposed, and possible scenarios for the gradual integration of quantum computing into RC architectures over the next 5–15 years have been considered, particularly as quantum processors become more compact and methods for integrating them into onboard systems improve. Thus, as existing barriers are overcome, quantum computers may eventually become an integral part of onboard control systems for RCs, providing a significant leap in their performance.