Image processing is one of the many computer domains that has seen quantum computing emerge as a revolutionary technology. This is due to the fact that quantum computing holds the potential of exponential speedups and unique techniques. The purpose of this study is to give a thorough analysis of the applications of quantum computing in image processing. It investigates the ways in which quantum algorithms and hardware might be used to handle traditional issues, such as picture compression, enhancement, pattern detection, and image recovery. Within the scope of this study, the theoretical underpinnings of quantum computing are investigated, recent developments in the field are discussed, and a comparison is made between quantum techniques and classical procedures. In addition to this, it outlines the primary obstacles, such as scalability and hardware limits, that are now preventing the broad use of quantum approaches in image processing. In conclusion, the study provides an overview of prospective future research areas, focusing on the possibility of further integrating quantum computing with sophisticated image processing technologies such as deep learning. The purpose of this work is to provide scholars and practitioners who are interested in the interface of quantum computing and image processing with a basic reference that they may use.
Image processing is one of the many computer domains that has seen quantum computing emerge as a revolutionary technology. This is due to the fact that quantum computing holds the potential of exponential speedups and unique techniques. The purpose of this study is to give a thorough analysis of the applications of quantum computing in image processing. It investigates the ways in which quantum algorithms and hardware might be used to handle traditional issues, such as picture compression, enhancement, pattern detection, and image recovery. Within the scope of this study, the theoretical underpinnings of quantum computing are investigated, recent developments in the field are discussed, and a comparison is made between quantum techniques and classical procedures. In addition to this, it outlines the primary obstacles, such as scalability and hardware limits, that are now preventing the broad use of quantum approaches in image processing. In conclusion, the study provides an overview of prospective future research areas, focusing on the possibility of further integrating quantum computing with sophisticated image processing technologies such as deep learning. The purpose of this work is to provide scholars and practitioners who are interested in the interface of quantum computing and image processing with a basic reference that they may use.
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.
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.
This paper delves into the groundbreaking potential of quantum computing, with a primary focus on qubits and their wide-ranging applications. It explores the fundamental principles of quantum physics, such as entanglement and superposition, demonstrating how these principles underpin innovative approaches to data representation and processing. Real-world demonstrations utilizing quantum hardware underscore the exponential advantages that quantum computing offers in terms of both speed and storage capacity. Within the realm of quantum computing, one major objective is to address the issue of decoherence, and the article proposes the utilization of photons as qubits due to their virtually infinite decoherence times. This introduction of Photonic quantum computing paves the way for a robust and scalable quantum computing approach, with specific objectives centered around enhancing the stability and scalability of quantum systems. Furthermore, the article explores the fascinating domain of quantum image processing, aiming to revolutionize the fields of image analysis and compression. Quantum image representations are envisioned to significantly enhance storage efficiency, and the article sets objectives to develop practical quantum algorithms for image processing tasks. To illustrate the practicality of these advancements, the article showcases a real-world implementation of quantum edge detection, emphasizing its promising applications not only in medical imaging but also in advancing artificial vision systems.
This survey report presents an overview of the field of quantum computing, focusing on the application of quantum technologies in machine learning, deep learning, image processing, and other related areas. A comprehensive analysis of research papers published between 2005 and 2022 is conducted, covering the latest advancements in quantum-based techniques. The report provides insights into various types of quantum technologies, including quantum machine learning, deep learning, computing, learning, and image processing. The survey encompasses a wide range of topics, such as quantum algorithms, quantum data encoding, quantum feature extraction, quantum neural networks, quantum image recognition, quantum optimization, and quantum simulations. It also identifies the emerging trends, challenges, and future research directions in this rapidly evolving field. The findings of this survey report are expected to provide valuable insights for researchers, practitioners, and decision-makers interested in the field of quantum and its applications in machine learning, deep learning, image processing, and related areas. Comprehensive analysis of the research papers and the identification of the current trends and challenges in this field can serve as a valuable reference for future research and development in the quantum computing domain.
This thorough overview paper investigates how quantum computing could revolutionize communication systems by resolving the intrinsic drawbacks of classical signal processing. Due to the rapid improvements in technology, these systems are subject to increasing demands for larger connection densities and improved spectrum efficiency, which presents substantial hurdles for conventional signal processing. The acceptance and expansion of modern communication technologies are impeded by these obstacles, which are typified by a trade-off between computing complexity and communication performance. Based on quantum mechanics, quantum computing offers a powerful substitute for classical algorithms by introducing a new paradigm that drastically lowers computational complexity for certain applications. This paper explores the history of quantization in classical communication signals and explores the use of quantum computing techniques in critical applications such as data detection, multi-user detection and channel estimation. The work showcases sophisticated methods for image denoising and Fourier transformations, expanding the conversation to include quantum processing of image signals. These quantum methods show significant promise for improving spectral analysis efficiency, reducing noise and increasing processing speed and accuracy. This study offers a thorough basis for future research and technical innovation in the field of image and conventional signal processing by outlining the current state of the art and possible uses of quantum computing in the future.
The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.