Principles and methodologies of quantum algorithmic gates design are described. The possibilities of quantum algorithmic gates simulation on classical computers are discussed. Applications of quantum gate of nanotechnology in intelligent control are introduced.

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**320**)Classification of quantum algorithms models is introduced. Simple models of quantum algorithms as decision making algorithms (Deutsch and Deutsch-Jozsa) are described. Basic programming techniques are discussed.

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**228**)Main benchmark’s gate design of quantum algorithms is introduced. Simulation results of quantum search algorithms on classical computers are described. Effective simulation methodology of quantum algo-rithms on classical computers are demonstrated.

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**272**)IT design of quantum algorithmic gates (QAG) is considered. General structures of the QAG design method and simulation system are introduced. Applications to efficient simulation of quantum algorithms (QA) on classical computer are described.

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**221**)Information dynamic analysis of main quantum algorithms is described. The qualitative analysis of quantum information is introduced. Measure of quantum algorithm’s computational intelligence is discussed.

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**207**)The simplest technique for simulating a quantum algorithm (QA) is described and is based on the direct matrix representation of the quantum operators. This approach is stable and precise, but it requires alloca-tion of operator’s matrices in the computer’s memory. Since the size of the operators grows exponentially, this approach is useful for simulation of QAs with a relatively small number of qubits (e.g., approximately 11 qubits on a typical desktop computer). Using this approach it is relatively simple to simulate the operation of a QA and to perform fidelity analysis. Two special search algorithms: Shor’s and Grover’s QA are described.

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**205**)This article describes how to build a classical hardware (HW) device, which accelerates the simulation of QA on classical computer. The usual approach for so doing consists in the simulation of the either QAs and their underlying quantum systems. The main aim of this article is not to work on real quantum HW (as quantum dots, ion traps, NMR etc.) but to take quantum computing as a computation paradigm (alternative to classical computing and soft computing).

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**226**)Different models of self-organization processes are described from physical, information, and algorith-mic (quantum computing) point of view. Role of quantum correlation types and information transport in self-organization of structure type design is discussed. A generalized quantum algorithm (QA) design of self-organization processes is developed. Particular case of this approach (based on early developed quantum swarm model) is described. Types of quantum operators as superposition, entanglement and interference in different models evolution of self-organization processes are introduced from quantum computing viewpoint. The physical interpretation of self-organization control process on quantum level is introduced based on the information model of the exchange and extraction of quantum (hidden) value information from/between clas-sical particle’s trajectories in particle swarm. New types of quantum correlations (as behavior control coor-dinator) and information transport (value information) between particle swarm trajectories are introduced.

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**243**)The structure of quantum fuzzy inference (QFI) model that realize the self-organization process are considered. QFI is one of possible realization of quantum control algorithm of the self-organization process-es that includes all of these features: (i) superposition; (ii) selection of quantum correlation types; (iii) in-formation transport and quantum oracle (dynamic evolution); and (iv) interference. QFI model is introduced based on thermodynamics and information-theoretic measures of agent interactions in communication space between macro- and micro-levels (the entanglement-assisted correlations in an active system represented by a collection of intelligent agents).

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**238**)A new problem – a Quantum PID controller design based on quantum inference (QI) from two K-gains (K1 and K2) of classical PID (with constant K-gains) controllers is investigate.

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**291**)Strategy of intelligent control systems based on quantum and soft computing technologies is considered. Quantum self-organization synergetic effects extracted from intelligent fuzzy controller’s knowledge bases interrelations introduced. The robustness of intelligent control systems in unpredicted control situations de-scribed with the mathematical and physical simulation of Benchmarks. Benchmarks demonstrated the intro-duction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems.

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**258**)As example of control object model port-controlled Hamiltonian systems are consider. Port-controlled Hamiltonian systems are the generalization of conventional Hamiltonian systems. They can describe not only mechanical systems but also a broad class of physical systems including passive electro-mechanical systems, hydraulic, gas, mechanical systems with non-holonomic constraints and their combinations. Port-controlled Hamiltonian systems describe, in a modular, network-like way, the interconnection of physical systems using the transfer of energy and entropy as the unifying concept. Thermodynamic trade – off between stability, controllability and robustness for described class of systems is introduced. Simulation of robust control based on quantum fuzzy inference is considered.

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