Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Sovremennye informacionnye tehnologii i IT-obrazovanie.

ИТ моделирования самоорганизующихся интеллектуальных контроллеров на основе квантового глубокого машинного обучения

Sergey Victorovich Ulyanov, Andrey G. Reshetnikov, Зрелова Д. П.

Published Jun 30, 2023• 0 citations
Publisher

Abstract

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 Lagrange/Hamilton neural networks. This work develops the approach of self-organized intelligent control, describing the strategy of designing intelligent cognitive control systems based on quantum and soft computing. The synergetic effect of the quantum self-organization of the knowledge base, extracted from the non-robust knowledge bases of the intelligent fuzzy controller, is demonstrated. The information-thermodynamic law of quantum self-organization of the optimal distribution of the basic qualities of control (stability, controllability and robustness) and the law of quantum information thermodynamics on the possibility of extracting additional useful work based on the extracted quantum information hidden in classical states are applied. Formed (without violating the second law of quantum thermodynamics) on the basis of the extracted amount of hidden quantum information, the "thermodynamic" control force allows the robot (as an object of control) to perform quantitatively more useful work compared to the amount of work spent (on extracting quantum hidden information). The guaranteed achievement of the goal of controlling the robot is carried out on the basis of a designed intelligent cognitive control system using the quantum knowledge base optimizer – QCOptKBTM, the structure of which includes a quantum fuzzy inference – QFI. The quantum algorithm of self-organization of non-robust QFI knowledge bases is structurally based on the synergetic effects of hidden quantum information to implement the optimal distribution of management qualities. This technology makes it possible to increase the reliability of intelligent cognitive control systems in control situations under uncertainty. The examples demonstrated the effectiveness of introducing the QFI scheme as a ready-made programmable algorithmic solution for embedded intelligent control systems. Обсуждается физическая интерпретация процесса управления самоорганизацией на квантовом уровне на основе квантовых информационно-термодинамических моделей обмена и извлечения квантовой (скрытой) ценностной информации из/между классическими траекториями частиц в модели «рой взаимодействующих частиц». Представлены и описываются основные физические и информационно-термодинамические аспекты модели квантового интеллектуального управления классическими объектами управления. Рассматривается подход построения эталонных моделей управления на основе новых законов квантового глубокого машинного обучения с применением квантовых нейронных сетей Лагранжа/Гамильтона. Данная работа развивает подход самоорганизующегося интеллектуального управления, описывая стратегию проектирования интеллектуальных систем когнитивного управления на основе квантовых и мягких вычислений. Продемонстрирован синергетический эффект квантовой самоорганизации базы знаний, извлеченный из не робастных баз знаний интеллектуального нечеткого регулятора. Применяется информационно-термодинамический закон квантовой самоорганизации оптимального распределения базисных качеств управления (устойчивость, управляемость и робастность) и закон квантовой информационной термодинамики о возможности извлечения дополнительной полезной работы на основе извлеченной квантовой информации, скрытой в классических состояниях. Сформированная (без нарушения второго закона квантовой термодинамики) на основе извлеченного количества скрытой квантовой информации «термодинамическая» сила управления позволяет роботу (как объекту управления) совершить количественно большую полезную работу по сравнению с количеством затраченной (на извлечение квантовой скрытой информации) работу. Гарантированное достижение цели управления роботом осуществляется на основе спроектированной интеллектуальной когнитивной системы управления с применением инструментария квантового оптимизатора баз знаний QCOptKBTM, в структуру которого включен квантовый нечеткий вывод – КНВ. Квантовый алгоритм самоорганизации не робастных баз знаний КНВ структурно опирается на синергетические эффекты от скрытой квантовой информации для осуществления реализации оптимального распределения качеств управления. Данная технология позволяет повысить надежность интеллектуальных когнитивных систем управления в ситуациях управления в условиях неопределенности. Примеры продемонстрировали эффективность введения схемы КНВ в качестве готового программируемого алгоритмического решения для встраиваемых интеллектуальных систем управления.

DOI
10.25559/sitito.019.202302.365-380

Related papers

Промышленная роботизированная интеллектуальная робастная система управления: применение технологий квантовых мягких вычислений и квантовой программной инженерии в неопределенных условиях управления

Сергей Викторович Ульянов, S.V. Ulyanov, А.Г. Решетников +3 more

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.

Международный журнал Программные продукты и системы•Mar 3, 2023•1 citation

Self-organized intelligent quantum controller: quantum deep learning and quantum genetic algorithm – QSCOptKBTM toolkit

Зрелова Д. П., V. Korenkov, Andrey Reshetnikov +2 more

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.

International Workshop on Deep Learning in Computational Physics (DLCP)•Nov 17, 2022•2 citations

Квантовый алгоритм робастного интеллектуального управления процессом охлаждения азотом сверхпроводящего магнита

А. Г. Решетников, С. В. Ульянов

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.

System Analysis in Science and Education•Jun 5, 2026•0 citations

Модели квантового «сильного» вычислительного интеллекта и квантовых нейронных сетей глубокого обучения: платформа интеллектуального управления промышленными роботизированными социотехническими системами

Капков Р. Ю., Olga Tyatyushkina, С.В. Ульянов

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.

•Sep 30, 2024•0 citations

Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds

Sergey Victorovich Ulyanov

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.

Artificial Intelligence Advances•Aug 9, 2021•7 citations

Information-thermodynamic uncertainty in intelligent self-organized fuzzy control systems and extractable work for thermodynamic control force from hidden quantum information

Сергей Викторович Ульянов, Дарья Петровна Зрелова, Алла Александровна Шевченко +1 more

Рассмотрена модель алгоритма квантовой самоорганизации робастной базы знаний для интеллектуальных нечетких регуляторов с требуемым уровнем робастности с информационно-термодинамической точки зрения. Предпосылкой модели является новая модель квантового вывода, основанная на квантовом генетическом алгоритме и разработке оптимальной структуры квантовой нейронной сети. Инструментарий квантового глубокого машинного обучения реализует разработанную платформу под названием «Оптимизатор базы знаний для квантовых вычислений» ($QCOptKB^{TM}$). Применение квантового алгоритма осуществляется в режиме реального времени для поиска типа квантовой корреляции между неизвестными решениями в квантовой суперпозиции неполных баз знаний интеллектуальных контроллеров, разработанных на основе программных вычислений (инструментарий $SCOptKB^{TM}$). Обсуждаются условия равновесия аналитических информационно-термодинамических компромиссных взаимосвязей между основными показателями качества управления (устойчивостью, управляемостью и робастностью) как новые законы проектирования робастного управления. Разработка интеллектуального управления с гарантированным достижением этих компромиссных взаимосвязей является конечной целью алгоритма квантовой самоорганизации несовершенной БЗ. Представлен новый синергетический квантовый информационный эффект: новая надежная интеллектуальная база знаний может быть создана в режиме онлайн из реакций на непредсказуемые ситуации управления любой несовершенной базой знаний с применением скрытой квантовой информации, извлеченной из квантовой корреляции. Более того, приведен новый закон формирования силы управления из квантовой термодинамики: на основе извлеченной (на микроуровне) скрытой квантовой информации из классических сигналов управления можно сформировать в режиме реального времени новую управляющую силу, которая может производить на макроуровне больше полезной работы, чем потери работы на извлечение этого количества скрытой квантовой информации. В рамках инструментария классического интеллектуального управления на основе только мягких вычислений достижение подобного синергетического информационного эффекта невозможно. Рассмотрены основные приложения для управления интеллектуальными когнитивными роботами. The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level from information-thermodynamic viewpoint considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm and design optimal structure of quantum neural network. Quantum deep machine learning toolkit applied developed platform titled as «Quantum Computing Optimizer of Knowledge Base» ($QCOptKB^{TM}$). Quantum algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing ($SCOptKB^{TM}$ toolkit). Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (stability, controllability and robustness) as new control design laws discussed. The smart control design with guaranteed achievement of these trade-off interrelations is the main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect introduced: a new robust smart knowledge base can be created on line from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Moreover, the new force control law from quantum thermodynamic described: with extracted hidden quantum information from classical control signal states (on micro-level) possible to design in on-line new control force that can produce on macro-level more value work amount than the work losses on the extraction of this amount of hidden quantum information. Within the toolkit of classical intelligent control based on soft computing the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.

Fuzzy Systems and Soft Computing•Dec 22, 2023•0 citations
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 Lagrange/Hamilton neural networks. This work develops the approach of self-organized intelligent control, describing the strategy of designing intelligent cognitive control systems based on quantum and soft computing. The synergetic effect of the quantum self-organization of the knowledge base, extracted from the non-robust knowledge bases of the intelligent fuzzy controller, is demonstrated. The information-thermodynamic law of quantum self-organization of the optimal distribution of the basic qualities of control (stability, controllability and robustness) and the law of quantum information thermodynamics on the possibility of extracting additional useful work based on the extracted quantum information hidden in classical states are applied. Formed (without violating the second law of quantum thermodynamics) on the basis of the extracted amount of hidden quantum information, the "thermodynamic" control force allows the robot (as an object of control) to perform quantitatively more useful work compared to the amount of work spent (on extracting quantum hidden information). The guaranteed achievement of the goal of controlling the robot is carried out on the basis of a designed intelligent cognitive control system using the quantum knowledge base optimizer – QCOptKBTM, the structure of which includes a quantum fuzzy inference – QFI. The quantum algorithm of self-organization of non-robust QFI knowledge bases is structurally based on the synergetic effects of hidden quantum information to implement the optimal distribution of management qualities. This technology makes it possible to increase the reliability of intelligent cognitive control systems in control situations under uncertainty. The examples demonstrated the effectiveness of introducing the QFI scheme as a ready-made programmable algorithmic solution for embedded intelligent control systems. Обсуждается физическая интерпретация процесса управления самоорганизацией на квантовом уровне на основе квантовых информационно-термодинамических моделей обмена и извлечения квантовой (скрытой) ценностной информации из/между классическими траекториями частиц в модели «рой взаимодействующих частиц». Представлены и описываются основные физические и информационно-термодинамические аспекты модели квантового интеллектуального управления классическими объектами управления. Рассматривается подход построения эталонных моделей управления на основе новых законов квантового глубокого машинного обучения с применением квантовых нейронных сетей Лагранжа/Гамильтона. Данная работа развивает подход самоорганизующегося интеллектуального управления, описывая стратегию проектирования интеллектуальных систем когнитивного управления на основе квантовых и мягких вычислений. Продемонстрирован синергетический эффект квантовой самоорганизации базы знаний, извлеченный из не робастных баз знаний интеллектуального нечеткого регулятора. Применяется информационно-термодинамический закон квантовой самоорганизации оптимального распределения базисных качеств управления (устойчивость, управляемость и робастность) и закон квантовой информационной термодинамики о возможности извлечения дополнительной полезной работы на основе извлеченной квантовой информации, скрытой в классических состояниях. Сформированная (без нарушения второго закона квантовой термодинамики) на основе извлеченного количества скрытой квантовой информации «термодинамическая» сила управления позволяет роботу (как объекту управления) совершить количественно большую полезную работу по сравнению с количеством затраченной (на извлечение квантовой скрытой информации) работу. Гарантированное достижение цели управления роботом осуществляется на основе спроектированной интеллектуальной когнитивной системы управления с применением инструментария квантового оптимизатора баз знаний QCOptKBTM, в структуру которого включен квантовый нечеткий вывод – КНВ. Квантовый алгоритм самоорганизации не робастных баз знаний КНВ структурно опирается на синергетические эффекты от скрытой квантовой информации для осуществления реализации оптимального распределения качеств управления. Данная технология позволяет повысить надежность интеллектуальных когнитивных систем управления в ситуациях управления в условиях неопределенности. Примеры продемонстрировали эффективность введения схемы КНВ в качестве готового программируемого алгоритмического решения для встраиваемых интеллектуальных систем управления.
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.
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.
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 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.
The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.
Рассмотрена модель алгоритма квантовой самоорганизации робастной базы знаний для интеллектуальных нечетких регуляторов с требуемым уровнем робастности с информационно-термодинамической точки зрения. Предпосылкой модели является новая модель квантового вывода, основанная на квантовом генетическом алгоритме и разработке оптимальной структуры квантовой нейронной сети. Инструментарий квантового глубокого машинного обучения реализует разработанную платформу под названием «Оптимизатор базы знаний для квантовых вычислений» (QCOptKBTMQCOptKB^{TM}QCOptKBTM). Применение квантового алгоритма осуществляется в режиме реального времени для поиска типа квантовой корреляции между неизвестными решениями в квантовой суперпозиции неполных баз знаний интеллектуальных контроллеров, разработанных на основе программных вычислений (инструментарий SCOptKBTMSCOptKB^{TM}SCOptKBTM). Обсуждаются условия равновесия аналитических информационно-термодинамических компромиссных взаимосвязей между основными показателями качества управления (устойчивостью, управляемостью и робастностью) как новые законы проектирования робастного управления. Разработка интеллектуального управления с гарантированным достижением этих компромиссных взаимосвязей является конечной целью алгоритма квантовой самоорганизации несовершенной БЗ. Представлен новый синергетический квантовый информационный эффект: новая надежная интеллектуальная база знаний может быть создана в режиме онлайн из реакций на непредсказуемые ситуации управления любой несовершенной базой знаний с применением скрытой квантовой информации, извлеченной из квантовой корреляции. Более того, приведен новый закон формирования силы управления из квантовой термодинамики: на основе извлеченной (на микроуровне) скрытой квантовой информации из классических сигналов управления можно сформировать в режиме реального времени новую управляющую силу, которая может производить на макроуровне больше полезной работы, чем потери работы на извлечение этого количества скрытой квантовой информации. В рамках инструментария классического интеллектуального управления на основе только мягких вычислений достижение подобного синергетического информационного эффекта невозможно. Рассмотрены основные приложения для управления интеллектуальными когнитивными роботами. The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level from information-thermodynamic viewpoint considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm and design optimal structure of quantum neural network. Quantum deep machine learning toolkit applied developed platform titled as «Quantum Computing Optimizer of Knowledge Base» (QCOptKBTMQCOptKB^{TM}QCOptKBTM). Quantum algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing (SCOptKBTMSCOptKB^{TM}SCOptKBTM toolkit). Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (stability, controllability and robustness) as new control design laws discussed. The smart control design with guaranteed achievement of these trade-off interrelations is the main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect introduced: a new robust smart knowledge base can be created on line from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Moreover, the new force control law from quantum thermodynamic described: with extracted hidden quantum information from classical control signal states (on micro-level) possible to design in on-line new control force that can produce on macro-level more value work amount than the work losses on the extraction of this amount of hidden quantum information. Within the toolkit of classical intelligent control based on soft computing the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.