Abstract Quantum image processing in qubit-based image representations for more than two dimensions seems to offer great opportunities for advanced data analysis and processing in quantum computers. Of fundamental importance is cycle shifting an image along an arbitrary direction, as various operations are based on it. In this work, a widely used image representation scheme is generalized into a single, multidimensional encoding. Within this framework, we define a cycle shifting method capable of translating a representation along any linear combination of its dimensional axes. The algorithmic procedure is appropriately redefined to accommodate arbitrary shifts in the Hilbert space. The corresponding computational complexity is optimized, achieving an exponential reduction in quantum resource requirements with respect to the shift-step magnitude. A comparative complexity analysis substantiates the efficiency gain, while simulation results validate the proposed multidimensional framework for two- and three-dimensional representations.
Abstract Quantum image processing in qubit-based image representations for more than two dimensions seems to offer great opportunities for advanced data analysis and processing in quantum computers. Of fundamental importance is cycle shifting an image along an arbitrary direction, as various operations are based on it. In this work, a widely used image representation scheme is generalized into a single, multidimensional encoding. Within this framework, we define a cycle shifting method capable of translating a representation along any linear combination of its dimensional axes. The algorithmic procedure is appropriately redefined to accommodate arbitrary shifts in the Hilbert space. The corresponding computational complexity is optimized, achieving an exponential reduction in quantum resource requirements with respect to the shift-step magnitude. A comparative complexity analysis substantiates the efficiency gain, while simulation results validate the proposed multidimensional framework for two- and three-dimensional representations.
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.
The quantum signal and image processing aims at reconfiguring the well-established signal representation and analysis methods to quantum-compatible computation models. The current state of quantum hardware availability and the expense of encoding high-dimensional image data confine the practical implementation of near-term quantum hardware. This work examines how classical signal-processing–based preprocessing can support quantum-oriented image representations in a computationally grounded manner. Handwritten digit images from the MNIST dataset are treated as discrete signals and normalized prior to analysis. Principal component analysis is used to evaluate how effectively informative content can be preserved under reduced- dimensional representations. The resulting feature dimensions are then mapped to corresponding qubit requirements under standard amplitude encoding assumptions, and classical logistic regression is employed to establish baseline classification performance across different dimensionalities. The results indicate that a substantial proportion of image variability is retained in low-dimensional subspaces, enabling compact representations compatible with limited qubit resources. Although dimensionality reduction introduces a gradual decline in classification accuracy, performance remains stable at moderate feature sizes, demonstrating a manageable trade-off between compression and discriminative capability. Overall, the findings highlight the value of integrating classical signal processing techniques into the preparatory stages of quantum image processing and provide a transparent reference for the development and evaluation of future hybrid quantum-classical methods.
Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log(2N) quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement.
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.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
This paper proposes a novel approach to information encoding within multi-dimensional spaces, leveraging the principles of quantum entanglement. The core idea is to construct multi-dimensional entangled states to encode multi-dimensional data directly into quantum bits (qubits). This method aims to overcome the limitations of traditional information encoding techniques, which typically restrict data representation to single dimensions. The proposed scheme utilizes the inherent correlations within entangled states to facilitate rapid data transmission and processing. We present a theoretical framework outlining the encoding process and discuss the potential advantages of this approach, specifically its ability to handle complex, high-dimensional datasets. The efficiency and scalability of this method are explored, highlighting its potential applications in areas such as advanced data storage, secure communication, and quantum computing. We focus on the mathematical formulation of the entanglement-based encoding process and demonstrate its feasibility through theoretical analysis.