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.
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 via the quantum platform is an emerging area for researchers. Researchers are more interested to move on towards quantum image processing instead of classical image processing. This chapter starts with the review of different quantum image computing-based research papers with a brief idea of the ethics which inspire quantum computing in the background and focus on the current scenario of recent trends of quantum image representation, pitfalls, and summarization of the pros and cons of it, with the limitations of the technologies used and focus on the recent work to be going on and application of it in a different field. In the next, it will focus on the different methods used by the researcher in the previous papers. The next section discussed the different methods based on quantum image representation used. Some different techniques of image storage, retrieval, and representation in a quantum system are discussed. Also, this chapter briefs the pros and cons of using different techniques in quantum systems in comparison to classical systems.
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.
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.
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 represents a paradigm shift in computational ability, offering exponentially faster processing for specific problems than traditional classical algorithms. This review provides a comprehensive assessment of the development and implications of algorithms specifically designed for quantum computing systems. Key quantum algorithms such as Shor’s algorithm for integer factorization and Grover’s algorithm for unstructured search have demonstrated potential quantum supremacy by outperforming their classical counterparts. We examine the theoretical underpinnings and practical challenges of implementing these algorithms, alongside exploring novel algorithmic frameworks emerging from quantum machine learning and error correction disciplines. Furthermore, the review discusses the profound implications of quantum algorithm development in cryptography, optimization, and simulation, which are poised to transform industries reliant on these computational tasks. Our analysis concludes with a perspective on future research directions and the barriers that must be overcome to realize the full potential of quantum algorithms in practical applications.
Quantum computing, with its promise of exponential speedups and novel algorithms, has emerged as a revolutionary technology in various computational fields, including image processing. This survey provides a comprehensive review of the applications of quantum computing in image processing, exploring how quantum algorithms and hardware can address classical challenges such as image compression, enhancement, pattern recognition, and image recovery. The paper delves into the theoretical foundations of quantum computing, discusses current advancements, and compares quantum methods with traditional approaches. Moreover, it identifies the key challenges, such as scalability and hardware limitations, that currently hinder the widespread adoption of quantum techniques in image processing. Finally, the survey outlines future research directions, highlighting the potential for further integration of quantum computing with advanced image processing technologies like deep learning. This work aims to serve as a foundational reference for researchers and practitioners interested in the intersection of quantum computing and image processing.