Quantum Image Representation (QIR) is a key element in Quantum Image Processing (QIP) and is essential for the effective management of quantum information. To process quantum images, it is necessary to convert classical digital image data into formats suitable for quantum computers. This conversion involves managing, manipulating, and storing the data efficiently in quantum states. The importance of QIR has spurred numerous studies based on the principles of quantum mechanics, aiming to develop effective methods for representing quantum images. These methods are vital for advancing quantum computing applications related to image processing. In this paper, we will discuss and compare two prominent techniques for encoding images into quantum states: the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR). These methods showcase different approaches to quantum image handing, highlighting the evolving nature of quantum image processing technology.
Quantum Image Representation (QIR) is a key element in Quantum Image Processing (QIP) and is essential for the effective management of quantum information. To process quantum images, it is necessary to convert classical digital image data into formats suitable for quantum computers. This conversion involves managing, manipulating, and storing the data efficiently in quantum states. The importance of QIR has spurred numerous studies based on the principles of quantum mechanics, aiming to develop effective methods for representing quantum images. These methods are vital for advancing quantum computing applications related to image processing. In this paper, we will discuss and compare two prominent techniques for encoding images into quantum states: the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR). These methods showcase different approaches to quantum image handing, highlighting the evolving nature of quantum image processing technology.
Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
Quantum Image Representation (QIR) has become a key component of Quantum Image Processing (QIP), enabling the encoding and manipulation of visual data within quantum computing environments.Its integration with Quantum Artificial Intelligence (QAI) and Quantum Machine Learning (QML) has attracted significant research interest due to the potential advantages of quantum parallelism and compact data representation.This survey reviews major QIR techniques, analyzes their encoding strategies and computational characteristics, and examines their opportunities, challenges, and future relevance in quantum AI and machine learning applications.A systematic review of 70 peer-reviewed journal and conference publications published between 1992 and 2025 was conducted.The selected studies were evaluated based on encoding mechanisms, qubit requirements, circuit complexity, retrieval capability, scalability, and suitability for quantum image processing and learning tasks.The review covers prominent QIR models, including FRQI, NEQR, MCQI, QPIE, GQIR, QRMW, and tensor-network-based representations.Comparative analysis reveals important trade-offs between qubit efficiency, encoding complexity, retrieval accuracy, and scalability.Amplitude-based approaches provide compact image encoding but suffer from probabilistic retrieval, whereas basis-state representations offer deterministic recovery at the expense of increased circuit complexity.QIR demonstrates significant potential in image classification, feature extraction, image compression, and hybrid quantum-classical learning frameworks.QIR provides a promising foundation for next-generation quantum-enhanced visual intelligence systems.However, practical implementation remains constrained by state preparation overhead, noise, decoherence, limited qubit availability, and the lack of standardized evaluation frameworks.Future research should focus on scalable encoding methods, fault-tolerant quantum architectures, benchmarking standards, and deeper integration with advanced QML models to accelerate the development of practical quantum image analytics.
This paper examines Quantum Image Representation Algorithms (QIRA), which have significantly advanced the encoding and manipulation of visual data using quantum computing. It explores key developments in image representation techniques, focusing on Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation (NEQR), Generalized Quantum Image Representation (GQIR), Bit Plane Representation of Quantum Images (BRQI), and Order Encoding Quantum Image Model (OQIM). Using the Qiskit framework, these methods are implemented to encode classical images into quantum states and perform various quantum operations. The paper provides a comprehensive understanding of QIRA by outlining the background of these representation techniques and rigorously evaluating their performance in image reconstruction. The evaluation is based on metrics such as Mean Squared Error (MSE), Cosine Similarity, Normalized Cross-Correlation (NCC), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).
Modern approaches to quantum image encoding are covered in this article, including LPIQE (Low-precision Quantum Image Encoding), FRQI (Functional Representation of Quantum Images), and NEQR (New Improved Quantum Representation). Because it enables effective picture processing on quantum computers with constrained resources, quantum image encoding is a crucial component of quantum computing. The page goes into great length on each method's working principles, benefits, and limitations, as well as the domains in which they can be applied. The benefits of representing binary and color images with quantum qubits are examined, along with the significance of encoding correctness and the effectiveness of employing quantum resources. Finally, the potential of quantum picture encoding for the advancement of data compression, image processing, and novel computing technologies is explored. Examples of algorithms, explanations of their properties, and potential uses in image processing and quantum computing are all covered in the article.
The basics of Quantum Information Processing (QIP) is summarized below, in addition to a comprehensive survey of quantum image-based data hiding techniques. QIP is more efficient and provides higher security than classical information processing. A comparative analysis of the Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR) model is also presented. QIP is used in various potential applications, hence offering an interesting and demanding area for researchers [1]. Some of the important applications include medicine, military, environmental monitoring, image processing, communications, computations, and cryptography. This article focusses on the processing of information that can be represented using quantum mechanics [2]. Furthermore, coherence, entanglement, and superposition of quantum states makes quantum computing more suitable to its classical counterpart for information storage and processing [1], [3].
In this paper, three techniques of internal image-representation in a quantum computer are compared: Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation of digital images (NEQR), and Quantum Boolean Image Processing (QBIP). All conspicuous technical items are considered in this comparison for complete analysis: i) performance as Classical-to-Quantum (Cl2Qu) interface, ii) characteristics of the employed qubits, iii) sparsity of the used internal registers, iv) number and size of the required registers, v) quality in the outcomes recovering, vi) number of required gates and its consequent accumulated noise, vi) decoherence, and vii) fidelity. These analyses and demonstrations are automatically extended to all variants of FRQI and NEQR. This study demonstrated the practical infeasibility in the implementation of FRQI and NEQR on a physical quantum computer (QPU), while QBIP has proven to be extremely successful on a) the four main quantum simulators on the cloud, b) two QPUs, and c) optical circuits from three labs. Moreover, QBIP also demonstrated its economy regarding the required resources needed for its proper function and its great robustness (immunity to noise), among other advantages, in fact, without any exceptions.