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ФИЗИКА ЭЛЕМЕНТАРНЫХ ЧАСТИЦ И АТОМНОГО ЯДРА

Моделирование квантового нечеткого вывода интеллектуальной системы управления охлаждением токовводов бустера ускорительного комплекса NICA

Zrelov, O. V. Ivantsova, M. S. Katulin, A. G. Reshetnikov, N. V. Ryabov, S. V. Ulyanov, P.V.

Published Jun 20, 2024• 0 citations
OpenAlex

Abstract

A new stage in the application of the previously developed quantum intelligent control technology requires a significant increase in the number of input parameters. The fundamental possibility of implementing a 30-qubit quantum fuzzy inference on a simulator with classical architecture is shown. A quantum scheme and the results of the operation of a quantum intelligent regulator for the robust control of the nitrogen flow in the cryogenic system of the booster measuring arm of the NICA accelerator complex are presented.

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Abstract A new stage in the application of the previously developed quantum intelligent control technology requires a significant increase in the number of input parameters. The paper shows the fundamental possibility of implementing a thirty-qubit quantum fuzzy inference on a simulator with classical architecture. A quantum scheme is presented and the results of the operation of a quantum intelligent regulator for the robust control the nitrogen flow in the cryogenic system of the booster measuring arm of the NICA accelerator complex are presented.

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•Sep 30, 2024•0 citations
A new stage in the application of the previously developed quantum intelligent control technology requires a significant increase in the number of input parameters. The fundamental possibility of implementing a 30-qubit quantum fuzzy inference on a simulator with classical architecture is shown. A quantum scheme and the results of the operation of a quantum intelligent regulator for the robust control of the nitrogen flow in the cryogenic system of the booster measuring arm of the NICA accelerator complex are presented.
Abstract A new stage in the application of the previously developed quantum intelligent control technology requires a significant increase in the number of input parameters. The paper shows the fundamental possibility of implementing a thirty-qubit quantum fuzzy inference on a simulator with classical architecture. A quantum scheme is presented and the results of the operation of a quantum intelligent regulator for the robust control the nitrogen flow in the cryogenic system of the booster measuring arm of the NICA accelerator complex are presented.
From the point view of quantum IT engineering, the definition of the concept and issues of intelligent control of weakly structured and poorly formalized objects with underdetermined/hidden parameters are discussed. The stages of domestic quantum IT software engineering for the design of embedded self-organizing intelligent control systems using the QSCIT (Quantum Soft Computational Intelligence Toolkit) software toolkit are presented. QSCIT is based on industrial knowledge base optimizers (SC&QC)OptKBTM and employs quantum soft computing technologies. A new type of coordinated dual-channel intelligent control of nitrogen pressure and flow valves with hidden feedback is considered in a process with a variable superconducting magnet cooling structure. In this case, the superconducting magnet is considered as a classical control object with a weakly structured, poorly formalized model with underdetermined parameters, information about which is contained in the learning signal measured on the actual object. Quantum technology for studying a highly nonlinear and dynamically unstable autonomous intelligent robot forms the basis of a system simulating the interaction of nitrogen pressure and flow valves, as well as an intelligent self-organizing quantum controller coordinating the control of nitrogen pressure and flow valves in a cryogenic superconducting magnet cooling unit in the NICA mega-science project (JINR). Quantum soft computing and quantum fuzzy inference in the quantum controller structure extract knowledge from the learning signal and, based on the extracted amount of hidden quantum information, generate additional "thermodynamic" control power as an additional information resource for the intelligent controller to perform useful work. These processes are implemented on a classical processor. The generation of this additional information resource is possible due to the existence (experimentally proven in the study) of a quantum synergistic effect of self-organization of imprecise knowledge bases: from a finite number of imprecise knowledge bases, quantum fuzzy inference generates a robust knowledge base in real time. An entropy analysis of the controllers' operation demonstrated the rational use of useful resources by the quantum intelligent controller, necessary to guarantee the achievement of the control goal. The efficiency and quantum advantage of using intelligent self-organizing quantum controllers (embedded in onboard intelligent control systems) to control classical control objects based on quantum soft computing technologies are demonstrated.
On concrete examples from robotics and self-organizing quantum controllers embedded in on-board control systems, the effectiveness and advantage of applying intelligent control systems using soft and quantum computing are shown. The results of the technology application are summarized on the examples of a dynamically unstable autonomous robot and a quantum regulator controlling the nitrogen pressure in the cryogenic cooling system of the superconducting magnet elements of the NICA plant. The possibility of transferring the existing technology to other control objects is shown. Quantum soft computing and quantum fuzzy inference are implemented on a classical processor and modeled on the “Govorun” supercomputer.
The strategy for designing intelligent control systems based on quantum and soft computing technologies is described.The synergetic effect of quantum self-organization of a robust knowledge base, extracted from imperfect knowledge bases of an intelligent fuzzy controller, is presented.The developed technology improves the reliability of intelligent cognitive control systems in unforeseen control situations, for example, with various types of interacting robots.Benchmarks demonstrated the effective implementation of a quantum fuzzy inference circuit as a readymade programmable algorithmic solution for lower-level control systems embedded in a standard board, demonstrated the quantum superiority of quantum intelligent control of classical control objects, expanding the Feynman-Manin thesis.The correct physical interpretation of the process of controlling self-organization at the quantum level is discussed on the basis of quantum information-thermodynamic models of exchange and extraction of quantum (hidden) valuable information from/between classical particle trajectories in the "swarm of interacting particles" model.A new information synergetic effect is demonstrated: a robust knowledge base of a quantum fuzzy controller is created in real time from two unreliable knowledge bases of a fuzzy controller.This effect is purely quantum in nature and uses hidden quantum information extracted from classical states.The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed.
Using the example of a multi-link robot manipulator, two ways of implementing fuzzy controllers were shown: one controller for all link controls and a separate fuzzy controller for each link control. A method is proposed to eliminate the mismatch of the operation of separate independent fuzzy controllers. The methods of self-organized coordination control based on quantum computing technologies for creating robust intelligent control systems for robotic manipulators with 3DOF and 7DOF are described. The quantum supremacy of the developed end-to-end IT design for modeling robust intelligent control systems is demonstrated.
The principles and methods of quantum «strong» computational intelligence model’s design based on the tools of quantum deep learning applying quantum neural networks and a quantum genetic algorithm are considered. Additional features of quantum perceptron models and quantum deep learning engineering models are discussed when using QCOptKBTM - knowledge base optimization toolkit for intelligent controllers in the tasks of quantum intelligent control of robotic sociotechnical systems in projects «Industry 4.0 / 5.0 /6.0». The description of the features of quantum deep learning allows to more accurately and deeply master the capabilities of the QCOptKBTM toolkit, which includes the stages of learning and extracting (from source data) a learning signal using the SCOptKBTM toolkit based on soft computing technology, and is further considered as classical data. Due to quantum computing operators, classical data is encoded by qubits, the optimal choice of quantum correlation between the desired solutions and the use of constructive interference is made, the desired result is extracted by measurement. Thus, the developed intelligent SCOptKBTM and QCOptKBTM tools include the principles of quantum deep learning, and, as in the case of soft computing technology, forms the optimal structure of the new quantum neural network, and through the use of a quantum genetic algorithm accelerates the search for the desired solution.