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.
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.
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.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
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.
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.
The advent of Noisy Intermediate-Scale Quantum (NISQ) machines has brought new opportunities for expanding machine learning abilities with the help of quantum computing. Herein, a complete hybrid quantum-classical learning system is introduced that incorporates classical preprocessing, quantum feature extraction, and classical machine learning in a strategic way to surpass the performance of existing systems for the tasks of complex pattern recognition. We perform large-scale experiments with two popular benchmark datasets: the Iris flower dataset and the Wisconsin Breast Cancer Diagnostic dataset. Our framework shows consistently improving accuracy of2−7%over stark classical methods without sacrificing practicality on state-of-the-art NISQ hardware. With rigorous ablation studies and cross-validation with cutting-edge approaches, we conclude that the best task distribution between traditional and quantum processors is key to near-term quantum advantage. Our introduced framework effectively achieves a quantum circuit depth reduction of 40 % using smart classical preprocessing along with improving classification performance using quantum-augmented feature representation. Our implementation offers both hardware-based execution with Qiskit as well as fast classical simulation modes, so it is available for researchers with limited quantum computing capabilities.
The quantum enhanced stereo vision technique is thus a groundbreaking effort toward autonomous robotics, where autonomous robots have high precision 3D mapping through the application of quantum computational principles to solve the problem of classical stereo vision systems. However, real-time robotic navigation and object recognition is not allowed by such traditional methods due to depth estimation errors, noise, and occlusion issues. Based on that, this research offers a Quantum Assisted Adaptive Stereo Vision System (QASVS) that makes use of quantum superposition to enhance the depth estimation parallelism, entanglement for noise reduction, and a quantum optimized disparity computation model to increase the mapping accuracy. The system makes significant improvements in depth perception and computing speed at a real-time adaptive level, which could not be accomplished by the above-mentioned methods. To test the proposed framework, it is tested in a dynamic environment and found to be performing better in low light and complex terrains. These systems can be most crucial in autonomous drones, robotic surgical systems, industrial automation, and space exploration because precise depth perception is necessary. Processing speed, image quality, and environmental adaptability are improved by comparing to traditional stereo vision techniques. The study foresees quantum computing as a revolution for 3D vision for robotics that in turn will lead to the quantum enhanced machine perception. The next step will be building quantum photonic processors, as well as applying AI-driven depth refinement for more optimization.