Quantum computation and quantum information is a new research field and interdiscipline of physics,mathematics,computer and information sciences.It is a wonderful combination of the theory of quantum mechanics and classical computation.It has been concerned by many national governments and research groups.Algorithm is the kernel of classical computation as well known,and this situation is the same to quantum computation.Currently,the most representative algorithms are only two.One is Shor's algorithm.The other is Grover's algorithm.The fast computation of set operation is very important,and it is the base of many aspects of sciences and techniques,such as database operation,image processing,signal processing.But,for the set with high-dimension unsorted vectors,the classical electronic computer can do nothing for the requirement of fast computation.Therefore,the new computation principle and new algorithm will be needed for set operations.Quantum image processing is a progress that uses quantum computer to process image and wants to get a better result than that of the electronic computer.It is a new born topic,which will be a hot research topic in the future.This paper presents a comprehensive statementabout quantum algorithm,quantum set operation,quantum image processing and quantum Hopfield neural network.
Quantum computation and quantum information is a new research field and interdiscipline of physics,mathematics,computer and information sciences.It is a wonderful combination of the theory of quantum mechanics and classical computation.It has been concerned by many national governments and research groups.Algorithm is the kernel of classical computation as well known,and this situation is the same to quantum computation.Currently,the most representative algorithms are only two.One is Shor's algorithm.The other is Grover's algorithm.The fast computation of set operation is very important,and it is the base of many aspects of sciences and techniques,such as database operation,image processing,signal processing.But,for the set with high-dimension unsorted vectors,the classical electronic computer can do nothing for the requirement of fast computation.Therefore,the new computation principle and new algorithm will be needed for set operations.Quantum image processing is a progress that uses quantum computer to process image and wants to get a better result than that of the electronic computer.It is a new born topic,which will be a hot research topic in the future.This paper presents a comprehensive statementabout quantum algorithm,quantum set operation,quantum image processing and quantum Hopfield neural network.
Quantum computing is revolutionizing every field of knowledge. With advent of affordable quantum computing almost possible the importance and relevance of quantum computing for all the recent research trends cannot be overemphasized. We have already witnessed quantum analogues of most celebrated classical algorithms starting from Shor’s algorithm for integer factorization, Grover’s search algorithms for unstructured database. Quantum Image Processing is an emerging field borne by applying quantum computing techniques to the Image Processing problems. This paper attempts to summarize and review the state of development in the field of Quantum Image Processing. All major quantum computing platforms have been presented, At the end a discussion on the current trends and future possibilities is presented.
Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing.This paper starts with a brief review of the principles which underlie quantum computing, and also deals with some of the basics of qubits and quantum computing.Then this paper starts to deal with some different methods of image storage, representation and retrieval in a quantum system.This paper also describes the advantages of using those methods in quantum systems compare to classical systems. In the next section, a short discussion on some of the important aspects, comparison among them and applications of quantum image processing is presented. A few other hot topics and open problems in quantum image processing are mentioned in this paper. This review article will provide the readership an overview of progress witnessed in the area of Quantum Image processing while also simulating further interest to pursue more advanced research in it.
Image processing is popular in our daily life because of the need to extract essential information from our 3D world, including a variety of applications in widely separated fields like bio-medicine, economics, entertainment, and industry. The nature of visual information, algorithm complexity, and the representation of 3D scenes in 2D spaces are all popular research topics. In particular, the rapidly increasing volume of image data as well as increasingly challenging computational tasks have become important driving forces for further improving the efficiency of image processing and analysis. Since the concept of quantum computing was proposed by Feynman in 1982, many achievements have shown that quantum computing has dramatically improved computational efficiency [1]. Quantum information processing exploit quantum mechanical properties, such as quantum superposition, entanglement and parallelism, and effectively accelerate many classical problems like factoring large numbers, searching an unsorted database, Boson sampling, quantum simulation, solving linear systems of equations, and machine learning. These unique quantum properties may also be used to speed up signal and data processing. In quantum image processing, quantum image representation plays a key role, which substantively determines the kinds of processing tasks and how well they can be performed.
Abstract Contemporarily, quantum computing has become a popular topic, which is a new approach that is different from classic computing. To be specific, it can deal with information by using mathematical modeling that used to represent quantum mechanics, including superposition, interference, and entanglement. In quantum Computing, information is storage in ‘qubit’, whereas classic computer use bit to store information. This article aims to introduce the principles and some algorithms of quantum computing, and then discuss some up-to-date important development. There are two important algorithms are mentioned in this article, Grover’s algorithms and Shor’s algorithms. According to the analysis, Grover’s algorithms is potentially faster than classic algorithms. However, it requires millions of qubits to make it. Besides, Shor’s algorithms have challenged classic encryption (RSA encryption). Two quantum computer Google Sycamore and Jiuzhang are also mentioned here. The interesting thing is, for some specific question, Jiuzhang performed better than classic computers. Finally, it is found out that the quantum computing is generally slower that classic computer now, due to the restriction of qubit numbers. However, quantum computing still has an advantage in some specific question. These results shed light on guiding further exploration of quantum computer designing.
The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encoding the visual data in quantum-mechanical systems as opposed to classical methods and by switching from classical to quantum information processing. Moreover, the quantum image processing is analysed based on the recent research works and is summarized. This study projects the application of quantum image processing and challenges.
Quantum computing exploits quantum mechanical properties to perform computations. It enables quantum parallelism and provides much more powerful data processing capabilities than classical computers. With a quantum computer, one can exponentially accelerate quantum system simulation and accelerate some important classical algorithms. Traditional quantum algorithms use unitary evolution to process information. A quantum computing process is the product of a series of unitary operators. In 1994, Shor invented a quantum prime factorization algorithm that exponentially accelerates the factorization of an integer. In 1996, Grover proposed a quantum search algorithm that accelerates the search of an unsorted database using square-root steps. Afterwards, the development of quantum algorithms slowed down, leading to a subsequent query by Shor in 2003 regarding why no more significant quantum algorithms have been found. Since 2009, many important new quantum algorithms have been proposed, such as a quantum algorithm for solving linear equations with the capability of exponential acceleration, a quantum algorithm for sparse Hamiltonian simulation using linear combinations of unitary operators, and a novel algorithm for Hamiltonian simulation, which provides exponential improvements in precision. In this paper, we first describe the basic principles of quantum computation. We then describe the Shor algorithm and Grover/Long search algorithm. These quantum algorithms are the general quantum algorithms that use unitary operations in their computational processes. Next, we introduce the basic principles of duality quantum computing, which was proposed in 2002. In contrast to traditional quantum computing, duality quantum computing allows the use of linear combinations of unitary operators in the computation process, meaning the multiplication, division, addition, and subtraction of unitary operators are all possible in duality quantum computing. Thus, duality quantum computing provides more flexibility for constructing quantum algorithms and the techniques used in classical algorithm design can be directly used to construct quantum algorithms. We then review recent work regarding newer quantum algorithms that enable the linear combination of unitary operators, which are actually duality quantum algorithms. Additionally, a duality quantum simulation algorithm for open quantum systems is introduced. This algorithm not only reduces computational complexity, but also improves accuracy exponentially. Finally, a summary and the future prospects of quantum algorithms are provided.