This article explores and develops a method for the functioning of quantum algorithms and models of quantum computing devices to transform a classical image into a quantum state, highlight boundaries and convert a halftone image into a binary one. The presented quantum algorithm demonstrates the prospects for applying quantum information theory to solve classical problems. The main emphasis of the work is on computer simulation of a quantum algorithm and the study of its effectiveness when using quantum computing tools and methods. The relevance of this research stems from the constant updating and expansion of the field of quantum research, as well as from the lack of computer simulations of quantum physical phenomena. The scientific novelty of the work lies in the development of an effective pattern recognition model using the properties and methods of quantum computing, which contributes to the development of this area of scientific research.
This article explores and develops a method for the functioning of quantum algorithms and models of quantum computing devices to transform a classical image into a quantum state, highlight boundaries and convert a halftone image into a binary one. The presented quantum algorithm demonstrates the prospects for applying quantum information theory to solve classical problems. The main emphasis of the work is on computer simulation of a quantum algorithm and the study of its effectiveness when using quantum computing tools and methods. The relevance of this research stems from the constant updating and expansion of the field of quantum research, as well as from the lack of computer simulations of quantum physical phenomena. The scientific novelty of the work lies in the development of an effective pattern recognition model using the properties and methods of quantum computing, which contributes to the development of this area of scientific research.
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.
Developing new computing methods and signal processing algorithms by borrowing from the principle of quantum mechanics is a very interesting and new research topic. By utilizing quantum algorithm in the process of computing, it is expected that this algorithm can solved the problems of image processing faster than using a classical algorithm. In this paper, a quantum digital image halftoning algorithm will be presented.The image would be processed using bitmap image (.bmp) and the applications would be created using Matlab R2009a. Test phase of the applications is using an image, lena.bmp. This application will show three new images as results of three halftoning methods: binary threshold method, ordered dithering method, and method with quantum algorithm. The method with quantum algorithm shows better than two others methods. Kata Kunci: image Processing, quantum Algoritm, halftoning method.
Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.
This article explores the use of quantum computing for image processing and inversion. The advantages of quantum data processing methods over classical methods are analyzed. A quantum circuit can solve the fundamental image processing task of image inversion. The application of quantum NOT gates to alter pixel states corresponding to grayscale image values was investigated. It has been demonstrated that using amplitude angles to encode pixel values enables the efficient creation of quantum superposition on an image. The solution involved generating a negative of the image using the X-gate and the Hadamard operation. A quantum circuit was designed and tested on a simulator; an algorithm was also proposed for converting measurement results into a finalized image. The study highlights the advantages and disadvantages of the quantum approach, the linearity of transformations, and scalability prospects. Quantum image processing algorithms can enhance performance when handling large datasets.
This article presents the most common image recognition algorithms implemented for a quantum neural network, consider them and draw a conclusion which algorithm is preferable to use in this or that case. Currently, there is a rapid increase in interest in quantum algorithms for pattern recognition, since the first quantum computers have already appeared. This is because the use of quantum algorithms since the advent of quantum computers has allowed an exponential increase in the speed of solving NP-complete problems. Paper decide the some problems of finding and creation of approaches for the functioning of quantum calculation processes and prototypes of quantum devices in the framework of quantum image processing. A method for storing and processing image components in quantum systems is proposed, which potentially will give a significant step toward in efficiency in comparison with existing classical ones and provide a solution to a number of complex problems.
The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum Algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image Processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era.