Электронный каталог Фундаментальной
библиотеки ФГБОУ ВО МГППУ

Книга
32. K

Kuravsky, L.S.
Quantum Representations and Their Applications in Diagnostics / L.S. Kuravsky. – Moscow : DeLibri, 2025. – 128 p. – На обороте титульного листа указан год издания 2024, а место издания и издательство - на русском языке. – 8,0 усл. печ. л. – Экземпляр № 66121 находится в Открытом доступе на Сретенке. – ISBN 978-5-4491-2308-4.

The formal quantum representations demonstrate significant capabilities for solving classification problems in studying dynamical behavior of stochastic systems. Among the features are entenglement by measuring, quantitative estimating the fit for a close qubit system and observations to identify qubit representation parameters, the specialized spectral metric for comparing patterns of system behaviors, with the multidimensional scaling, cluster analysis and discriminal analysis being applied to the matrices of mutual distances calculated using the represented spectral metric. Hidden evolution periodicities during the observatioin time period are determined by means of the quantum systems spectral analyses to clarify the system behavior structure. Applied quantum representations are purely theoretical constructs and do not require special computational tools for their implementation, providing quantum calculations in the usual sense. These representations can be used for representing the Markovian processes with the aid of qubit (i.e. 2-state quantum) representations of staying in the corresponding states. Presented also is a mathematical model of related activity designed to obtain comparative assessments of the activities of complex technical systems operators, representing their activity through a probability matrix of transitions between the performed elementary operations. The feasibility of thid model is due to the high efficirncy of quantum spectral analysis of transition probability matrices in combination with quantum filtering when solving classification problems. The study showed that with a sufficirnt volume of empirical data, even a one-time manifistation of some regularity indicates its presence and the regularity index value can measure the reliability of such a conclusion. The presented mathematical tools provide higher efficiency than other relevant approaches, obtaining diagnostic conclusions using empirical data of much smaller volume and extracting more helpful information. The given numerous illustrative examples of practial applications, mainly civil aviation ones, clarify diagnostic benefits of hte proposed techniques. The book is intended for both specialists in the field of applied mathematics and other experts involved in solving diagnostic problems.

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Общий = Информатика, вычислительная техника