40 research outputs found
Quantum Algorithmic Gate-Based Computing: Grover Quantum Search Algorithm Design in Quantum Software Engineering
The difference between classical and quantum algorithms (QA) is following:
problem solved by QA is coded in the structure of the quantum operators. Input
to QA in this case is always the same. Output of QA says which problem coded.
In some sense, give a function to QA to analyze and QA returns its property as
an answer without quantitative computing. QA studies qualitative properties of
the functions. The core of any QA is a set of unitary quantum operators or
quantum gates. In practical representation, quantum gate is a unitary matrix
with particular structure. The size of this matrix grows exponentially with an
increase in the number of inputs, which significantly limits the QA simulation
on a classical computer with von Neumann architecture. Quantum search algorithm
(QSA) - models apply for the solution of computer science problems as searching
in unstructured data base, quantum cryptography, engineering tasks, control
system design, robotics, smart controllers, etc. Grovers algorithm is explained
in details along with implementations on a local computer simulator. The
presented article describes a practical approach to modeling one of the most
famous QA on classical computers, the Grover algorithm.Comment: arXiv admin note: text overlap with arXiv:quant-ph/0112105 by other
author
Fast quantum search algorithm modelling on conventional computers: Information analysis of termination problem
The simplest technique for simulating a quantum algorithm - QA described
based on the direct matrix representation of the quantum operators. Using this
approach, it is relatively simple to simulate the operation of a QA and to
perform fidelity analysis. A more efficient fast QA simulation technique is
based on computing all or part of the operator matrices on an as needed current
computational basis. Using this technique, it is possible to avoid storing all
or part of the operator matrices. The compute on demand approach benefits from
a study of the quantum operators, and their structure so that the matrix
elements can be computed more efficiently. Effective simulation of Grover
quantum search algorithm as example on computer with classical architecture is
considered
Robust PID Controller Design on Quantum Fuzzy Inference: Imperfect KB Quantum Self-Organization Effect-Quantum Supremacy Effect
Quantum PID controller design based on quantum fuzzy inference from two K-gains (Â and ) of classical PID (with constant K-gains) controllers investigated. Computational intelligence toolkit as soft computing technology in learning situations applied. Quantum approach performance in design of robust conventional controllers as intractable classical task of control system theory demonstrated. Simulation of intelligent control Benchmark demonstrated
Intelligent robust control of redundant smart robotic arm Pt I: Soft computing KB optimizer - deep machine learning IT
Redundant robotic arm models as a control object discussed. Background of computational intelligence IT based on soft computing optimizer of knowledge base in smart robotic manipulators introduced. Soft computing optimizer is the toolkit of deep machine learning SW platform with optimal fuzzy neural network structure. The methods for development and design technology of intelligent control systems based on the soft computing optimizer presented in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data, and in the presence of stochastic noises of various physical and statistical characters. The knowledge bases formed with the application of a soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object. The robustness of control laws is achieved by application a vector fitness function for genetic algorithm, whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system, and the other components describe conventional control objective functionals such as minimum control error, etc. The application of soft computing technologies (Part I) for the development a robust intelligent control system that solving the problem of precision positioning redundant (3DOF and 7 DOF) manipulators considered. Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described
Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application
Strategy of intelligent cognitive control systems based on quantum and soft
computing presented. Quantum self-organization knowledge base synergetic effect
extracted from intelligent fuzzy controllers imperfect knowledge bases
described. That technology improved of robustness of intelligent cognitive
control systems in hazard control situations described with the cognitive
neuro-interface and 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
possibility of neuro-interface application based on cognitive helmet with
quantum fuzzy controller for driving of the vehicle is shown
Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach
Abstract. The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
Robotic Smart Prosthesis Arm with BCI and Kansei / Kawaii / Affective Engineering Approach. Pt I: Quantum Soft Computing Supremacy
A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given. As a result, a prototype of manmade prosthesis on a 3D printer as well as a foundation for computational intelligence presented. The application of soft computing technology (the first step of IT) allows to extract knowledge directly from the physical signal of the electroencephalogram, as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state. The possibilities of applying quantum soft computing technologies (the second step of IT) in the processes of robust filtering of electroencephalogram signals for the formation of mental commands and quantum supremacy simulation of robotic prosthetic arm discussed
Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system
Design, Performance, and Calibration of CMS Hadron Endcap Calorimeters
Detailed measurements have been made with the CMS hadron calorimeter endcaps (HE) in response to beams of muons, electrons, and pions. Readout of HE with custom electronics and hybrid photodiodes (HPDs) shows no change of performance compared to readout with commercial electronics and photomultipliers. When combined with lead-tungstenate crystals, an energy resolution of 8\% is achieved with 300 GeV/c pions. A laser calibration system is used to set the timing and monitor operation of the complete electronics chain. Data taken with radioactive sources in comparison with test beam pions provides an absolute initial calibration of HE to approximately 4\% to 5\%