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Journal of Physics Conference Series

Using quantum computing to create efficient algorithms

Ирина Николаевна Балаба, Galina Deryabina, I. Pinchuk, I V Sergeev, S B Zabelina

Published Oct 1, 2021• 0 citations
Publisher

Abstract

Abstract The article presents a historical overview of the development of the mathematical idea of a quantum computing model - a new computational strategy based on the postulates of quantum mechanics and having advantages over the traditional computational model based on the Turing machine; clarified the features of the operation of multi-qubit quantum systems, which ensure the creation of efficient algorithms; the principles of quantum computing are outlined and a number of efficient quantum algorithms are described that allow solving the problem of exponential growth of the complexity of certain problems.

DOI
10.1088/1742-6596/2056/1/012059

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Abstract The article presents a historical overview of the development of the mathematical idea of a quantum computing model - a new computational strategy based on the postulates of quantum mechanics and having advantages over the traditional computational model based on the Turing machine; clarified the features of the operation of multi-qubit quantum systems, which ensure the creation of efficient algorithms; the principles of quantum computing are outlined and a number of efficient quantum algorithms are described that allow solving the problem of exponential growth of the complexity of certain problems.
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Quantum computing is an emerging paradigm that leverages the principles of quantum mechanics to solve computational problems beyond the reach of classical computers. This article provides an overview of the fundamental concepts of qubits, the distinctive features of quantum mechanics such as superposition and entanglement, and the challenges of building scalable, fault-tolerant systems. It surveys key quantum algorithms and their potential applications in fields including cryptography, optimization, finance, chemistry, and machine learning. Additionally, it highlights the importance of verification frameworks for ensuring the reliability of quantum programs. A literature review of significant contributions is presented, drawing insights from recent surveys on quantum algorithms, qubit technologies, and software verification approaches. The article concludes by discussing ongoing challenges, such as error correction overhead, hardware scalability, and verification complexity, and suggests directions for future research.
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Abstract The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.