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Physics of Particles and Nuclei

Simulation of the Quantum Fuzzy Inference of the Intelligent Control System for Cooling the Current Leads of the Booster of the NICA Accelerator Complex

Petr Zrelov, Иванцова О. В., Катулин Михаил Сергеевич, Andrey G. Reshetnikov, Nikita Ryabov, Sergey Victorovich Ulyanov

Published Jun 1, 2024• 3 citations
Publisher

Abstract

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.

DOI
10.1134/s1063779624030997

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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.
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 On concrete examples from robotics and self-organized 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.
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.
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.
The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.
Strategy of intelligent cognitive control systems of ill-defined control objects based on quantum and soft computing presented. Quantum self-organization knowledge base synergetic effect extracted from intelligent fuzzy controller’s imperfect knowledge bases described. That technology improved of robustness of intelligent cognitive control systems in hazard control situations described with different types of robot cooperation. Examples demonstrated the introduction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems. The 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. The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed and described. An approach is considered for constructing reference control models based on new laws of quantum deep machine learning applying neural networks in mega-science project NICA.