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).
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).
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.
This study presents a comparative evaluation of three quantum image representation techniques: FRQI, QPIE, and NEQR. The Baboon image is used as a benchmark, and performance is assessed through PSNR, MSE, SSIM, and execution time. Simulations were conducted using a noiseless quantum simulator to ensure fair comparison across methods. Results indicate that QPIE achieves the best reconstruction quality, with PSNR = 117.97, MSE = 1.037, and SSIM = 0.975 for the 8 x 8 image, while maintaining strong performance for larger sizes. In contrast, FRQI shows lower quality and high execution time. NEQR is computationally faster (4.85 s) but delivers moderate quality (PSNR = 79.38, SSIM = 0.859). Additionally, QPIE requires fewer qubits, making it more resource-efficient. Overall, QPIE emerges as the most effective representation, striking a balance between image quality, computational cost, and qubit utilization. These findings highlight QPIE as a promising candidate for future quantum image processing tasks such as compression, edge detection, and negative transformation.
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.
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 is based on principles of quantum computing to represent visual information effectively, preparation of a quantum state plays a crucial rule in circuit performance. In Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise and decoherence predominantly affect the fidelity of image-encoded quantum states. This paper deals with a comparative analysis of Four quantum image encoding schemes namely, Flexible Representation of Quantum Images (FRQI), Quantum Probability Image Encoding (QPIE), Order-Encoded Quantum Image Model (OQIM), and Enhanced Flexible Representation of Quantum Images (EFRQI). Noise free and Noisy state preparation simulations are executed using gate-based quantum simulators in the Pennylane environment, incorporating Bit Flip (BF), Phase Flip (PF), Amplitude Damping (AD), Phase Damping (PD), and Depolarizing noise (DN) channels. Image related performance metrics such as, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) in dB, Structural Similarity Index Measure (SSIM), and Intersection over Union (IoU) are used to measure the performance of image encoded quantum states. Our results reveal that distinct noise robustness characteristics’ across various encoding methods reveals the importance of Noise aware encoding selection for NISQ era quantum image processing applications.
Quantum Image Processing is based on principles of quantum computing to represent visual information effectively, preparation of a quantum state plays a crucial rule in circuit performance. In Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise and decoherence predominantly affect the fidelity of image-encoded quantum states. This paper deals with a comparative analysis of Four quantum image encoding schemes namely, Flexible Representation of Quantum Images (FRQI), Quantum Probability Image Encoding (QPIE), Order-Encoded Quantum Image Model (OQIM), and Enhanced Flexible Representation of Quantum Images (EFRQI). Noise free and Noisy state preparation simulations are executed using gate-based quantum simulators in the Pennylane environment, incorporating Bit Flip (BF), Phase Flip (PF), Amplitude Damping (AD), Phase Damping (PD), and Depolarizing noise (DN) channels. Image related performance metrics such as, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) in dB, Structural Similarity Index Measure (SSIM), and Intersection over Union (IoU) are used to measure the performance of image encoded quantum states. Our results reveal that distinct noise robustness characteristics’ across various encoding methods reveals the importance of Noise aware encoding selection for NISQ era quantum image processing applications.