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.
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.
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.
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.
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.
Quantum computing has emerged as a transformative paradigm, with revolutionary potential in numerous fields, including quantum image processing and compression. Applications that depend on large scale image data could benefit greatly from parallelism and quantum entanglement, which would allow images to be encoded and decoded with unprecedented efficiency and data reduction capability. This paper provides a comprehensive overview of the rapidly evolving field of quantum image compression, including its foundational principles, methods, challenges, and potential uses. The paper will also feature a thorough exploration of the fundamental concepts of quantum qubits as image pixels, quantum gates as image transformation tools, quantum image representation, as well as basic quantum compression operations. Our survey shows that work is still sparse on the practical implementation of quantum image compression algorithms on physical quantum computers. Thus, further research is needed in order to attain the full advantage and potential of quantum image compression algorithms on large-scale fault-tolerant quantum computers.
Quantum computing is increasingly being viewed as a promising direction for the future of image processing. Conventional approaches to image classification, enhancement, compression, and restoration, although highly developed, often struggle with scalability, computational complexity, and data security—especially when processing high-dimensional or sensitive visual information. This survey explores how quantum computing can help address these limitations through quantum image representation models such as FRQI and NEQR, variational quantum circuits, and hybrid quantum–classical architectures. The primary objective of this review is to present a clear and structured overview of the current state of quantum image processing (QIP). It discusses foundational representation models, emerging hybrid learning techniques, available software frameworks and simulators, benchmarking practices, and practical applications in areas such as medical imaging, remote sensing, and biometric security. Particular attention is given to solutions that are feasible on today’s NISQ-era hardware, including hybrid architectures and quantum-secured image processing schemes. The survey concludes by identifying existing challenges and outlining realistic future research directions. Overall, it emphasizes the practical importance of hybrid models, the need for standardized quantum-oriented benchmarks, and the growing potential of quantum-based security mechanisms for safeguarding visual data.
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.