Quantum information technologies are reshaping communication systems, but reliable integration of classical data, particularly images, into quantum frameworks remains a challenge. Circuit-based quantum image encoding methods are susceptible to noise-induced degradation, which undermines reproducibility and reliability. Here, we investigate the impact of quantum noise channels across the encoding and measurement stages within a hybrid quantum-classical distributed system. We also introduce noise into selected gate operations and measurements to demonstrate its distinct effects on the quantum circuit. To quantify the results, we compute the mean squared error (MSE), the structural similarity index measure (SSIM), and the peak signal-to-noise ratio (PSNR), and benchmark the results across various settings of the noise parameter. Finally, we present experimental results highlighting how image complexity affects encoding robustness using the Linnaeus and MNIST datasets. We anticipate that the presented results will contribute to the development of dependable quantum-classical orchestration strategies for resilient image processing.
Quantum information technologies are reshaping communication systems, but reliable integration of classical data, particularly images, into quantum frameworks remains a challenge. Circuit-based quantum image encoding methods are susceptible to noise-induced degradation, which undermines reproducibility and reliability. Here, we investigate the impact of quantum noise channels across the encoding and measurement stages within a hybrid quantum-classical distributed system. We also introduce noise into selected gate operations and measurements to demonstrate its distinct effects on the quantum circuit. To quantify the results, we compute the mean squared error (MSE), the structural similarity index measure (SSIM), and the peak signal-to-noise ratio (PSNR), and benchmark the results across various settings of the noise parameter. Finally, we present experimental results highlighting how image complexity affects encoding robustness using the Linnaeus and MNIST datasets. We anticipate that the presented results will contribute to the development of dependable quantum-classical orchestration strategies for resilient image processing.
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.
The rapid advancement of quantum information science is reshaping communication technologies, yet it also introduces unique challenges, particularly from quantum noise channels that significantly degrade circuit-based quantum image encodings. Here, we design and investigate a distributed quantum circuit architecture to mitigate channel noise effects in quantum-based representations of classical images. We evaluate the approach across multiple noise models using the structural similarity index measure (SSIM) and demonstrate that increasing the number of quantum circuit nodes significantly enhances image quality. Our results highlight the potential of distributed quantum circuits to support scalable and resilient quantum image processing.
The fusion of quantum computing with image processing has significantly improved both computational efficiency and fidelity in amplitude-encoded image reconstruction. While prior investigations have delved into advancements in quantum-to-classical data decoding, there remains a dearth of research addressing hybrid quantum-classical architectures and strategies to enhance noise resilience in quantum image processing. This study evaluates the implementation of three quantum transforms: Quantum Fourier Transform (QFT), Quantum Wavelet Transform (QWT), and Quantum Principal Component Analysis (QPCA), focusing on their efficacy in reconstructing images subjected to Gaussian and salt-and-pepper noise. Reconstruction quality is quantified through metrics such as error rates, similarity indices, and uncertainty quantification metrics, supported by a sensitivity analysis to ascertain robustness under varying noise conditions. The results demonstrate that QWT consistently achieves the highest Structural Similarity Index Measure (SSIM), while QFT often yields negative histogram similarity values, indicating significant alterations in color distribution. For instance, when analyzing one image, QWT exhibited a moderate decrease in Peak Signal-to-Noise Ratio (PSNR) from 37.83 to 37.71 with noise introduced, whereas QFT's PSNR experienced a more pronounced decline from 38.70 to 38.48. The study elucidates an inverse relationship between Mean Squared Error (MSE) and PSNR, underscoring the adverse effects of noise on image quality. Notably, QPCA was observed to produce the lowest Euclidean distance, signifying a strong retention of image integrity. These findings underscore the importance of judiciously selecting quantum methodologies tailored to specific application requirements, particularly in critical domains such as medical imaging, where precision and accuracy are paramount.
Purpose: This work aims to apply Quantum Hilbert Scrambling to enhance the security and integrity of image watermarking without affecting visual quality degradation. Further conception of the surveyed methods could result in a very good solution to conventional methods of watermarking in solving some problems of digital image security and integrity with new concepts of quantum computing. Methods: The paper reviews Quantum Hilbert Scrambling, whose computational complexity is . The process involves encoding the image into a quantum state, permuting qubits by the Hilbert curve, and embedding a watermark using quantum gates. Result: The quantitative performance evaluation metrics, like Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), have shown high Peak Signal to Noise Ratio (PSNR) values from 56.13 dB to 57.87 dB and Structural Similarity Index (SSIM) from 0.9985 to 0.9990, correspondingly. This justifies the fact that the quality degradation is very slight and the fine details of the structure are well maintained. Novelty: The proposed method uniquely integrates quantum computing with traditional watermarking steps for a secure and effective approach in digital watermarking. Further development should focus on improving the quantum circuit regarding computation efficiency, extending the applicability of the method to a wide range of images, and various situations in watermarking, and finding hybrid approaches by combining quantum and classical approaches towards better performance and scalability.
Synthetic Aperture Radar (SAR) imaging is crucial in remote sensing due to its ability to produce high-resolution images regardless of weather conditions or daylight. However, SAR images often suffer from various types of noise, especially speckle noise, which degrades image quality and complicates data analysis. Traditional noise reduction techniques face challenges in balancing noise suppression with the preservation of image details. Recent deep learning approaches, such as U-Net architectures, have made strides in addressing these issues. Simultaneously, quantum computing has emerged as a promising field that can potentially enhance computational methods in image processing. We propose a hybrid classical-quantum U-Net framework that integrates quantum-classical algorithms with tensor networks for improved noise reduction in SAR images. Leveraging the representational capacity of tensor networks and the computational strengths of quantum algorithms, our method aims to surpass the limitations of classical techniques in managing complex noise patterns. In our architecture, certain layers of the U-Net are implemented using quantum circuits specifically optimized for noise suppression. The inclusion of tensor networks enables efficient handling of high-dimensional data within the hybrid model. The idea is to perform extensive experiments on standard SAR datasets to demonstrate that our hybrid U-Net outperforms traditional denoising methods and purely classical deep learning models in both noise reduction and detail preservation. Quantitative assessments using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) would be used for comparison. Moreover, we will evaluate the computational efficiency of our approach, underscoring the potential of quantum computing to expedite complex image processing tasks. This work aims to highlight the feasibility and benefits of integrating quantum computing into practical remote sensing applications. Our hybrid quantum-classical U-Net architecture with tensor networks paves the way for advanced noise reduction techniques in SAR imaging and potentially other domains. Future research will focus on optimizing quantum circuit designs for specific noise types and exploring the scalability of our approach alongside advancements in quantum hardware.