This work presents QuantumVision, a conceptual hybrid quantum–classical framework for three-dimensional (3D) image reconstruction from partial two-dimensional views. Recent deep learning approaches such as Large Reconstruction Models (LRM) and diffusion-based 3D methods achieve high reconstruction quality but suffer from extreme computational costs and scalability limitations. QuantumVision explores the integration of Quantum Image Processing (QIP) techniques as preprocessing and optimization aids within classical 3D reconstruction pipelines. The framework analyzes computational bottlenecks in modern reconstruction systems and identifies stages where quantum-assisted methods—such as quantum edge detection, amplitude-based feature encoding, and variational quantum circuits—could theoretically improve efficiency and robustness. This paper does not claim experimental quantum advantage; instead, it provides a structured and realistic conceptual foundation for future research on quantum-enhanced 3D reconstruction. The work is intended for researchers and students interested in computer vision, hybrid quantum–classical machine learning, and emerging applications of quantum computing in visual perception.
This work presents QuantumVision, a conceptual hybrid quantum–classical framework for three-dimensional (3D) image reconstruction from partial two-dimensional views. Recent deep learning approaches such as Large Reconstruction Models (LRM) and diffusion-based 3D methods achieve high reconstruction quality but suffer from extreme computational costs and scalability limitations. QuantumVision explores the integration of Quantum Image Processing (QIP) techniques as preprocessing and optimization aids within classical 3D reconstruction pipelines. The framework analyzes computational bottlenecks in modern reconstruction systems and identifies stages where quantum-assisted methods—such as quantum edge detection, amplitude-based feature encoding, and variational quantum circuits—could theoretically improve efficiency and robustness. This paper does not claim experimental quantum advantage; instead, it provides a structured and realistic conceptual foundation for future research on quantum-enhanced 3D reconstruction. The work is intended for researchers and students interested in computer vision, hybrid quantum–classical machine learning, and emerging applications of quantum computing in visual perception.
Building a 3D scene from a limited number of images remains a challenging problem in computer vision, particularly for applications such as augmented reality, robotics, medical imaging, and autonomous navigation. Existing deep learning models like LRM and vFusion3D achieve high-quality reconstructions but require significant computational resources and long inference times, making them difficult to deploy on edge devices. This research introduces QuantumVision, a hybrid quantum-classical framework that integrates quantum image-processing techniques into a traditional 3D reconstruction pipeline. The framework consists of three modular components: quantum-assisted preprocessing, amplitude-based feature encoding, and a variational quantum optimization stage. These modules are designed to integrate with classical workflows while leveraging the potential advantages of Noisy Intermediate-Scale Quantum (NISQ) devices. Experimental simulations using Qiskit demonstrate improved feature extraction, edge detection, and reconstruction fidelity compared to classical baselines. The proposed approach highlights the potential of hybrid quantum computing to enhance computational efficiency and accuracy in partial-view 3D reconstruction tasks while remaining compatible with current quantum hardware limitations.
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.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
Abstract Image segmentation remains one of the most extensively studied problems in computer vision, with applications ranging from medical image analysis and autonomous driving to remote sensing and industrial inspection. While deep learning approaches currently dominate the field, interest in quantum computing has motivated the development of quantum and quantum-inspired segmentation techniques that explore alternative strategies for image representation, optimization, and learning. This survey provides a comprehensive review of quantum and quantum-inspired image segmentation methods within the broader context of modern segmentation research. To establish this context, we first examine the evolution of image segmentation methodologies, covering classical image processing techniques, machine learning approaches, and contemporary deep learning architectures.We then organize the existing quantum image segmentation literature into four categories: quantum-inspired, tensor network-based, hybrid quantum-classical, and fully quantum methods. Across these categories, we analyze computational principles, image encoding and representation strategies, application domains, and datasets. We further compare quantum and quantum-inspired approaches with established segmentation paradigms in terms of supervision, scalability, and hardware requirements. Our analysis indicates that quantum-inspired methods currently represent the most developed direction, while hybrid approaches are emerging and fully quantum methods remain constrained by quantum hardware, data encoding costs, and scalability challenges. By connecting quantum image segmentation with the broader evolution of segmentation research, this survey provides a unified perspective on the field. It identifies current trends, open challenges, and promising future directions for researchers exploring the emerging role of quantum computing in image segmentation.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.
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.