The exponential growth of high-resolution video content across domains such as surveillance, healthcare, satellite imaging, and streaming has intensified computational and storage demands, exposing the limitations of classical video processing architectures. In response, this paper introduces a novel hybrid quantum-classical video processing framework that leverages the strengths of quantum algorithms such as the Quantum Fourier Transform (QFT), Grover’s Search, and Variational Quantum Circuits (VQCs) for enhanced video compression, feature extraction, object detection, and secure transmission. Unlike conventional GPU-intensive architectures, our approach offloads critical tasks to quantum simulators, exploiting quantum superposition and entanglement to reduce processing latency by 27% and compression overhead by up to 35%, as shown through simulation benchmarks. A key contribution of this work is the integration of quantum encoding and entropy-aware compression using amplitude and angle-based encoding techniques optimized for NISQ-era devices. MATLAB-based quantum-inspired simulation shows that quantum-processed edge detection improved accuracy by 19.6% compared to classical Laplacian filtering in grayscale frames. Experimental validation using IBM Qiskit, Google Cirq, and Xanadu PennyLane supports the feasibility of hybrid models. This framework opens the door to scalable, secure, and real-time multimedia applications such as 4K+ real-time analytics, quantum-secure streaming, and deepfake detection.
The exponential growth of high-resolution video content across domains such as surveillance, healthcare, satellite imaging, and streaming has intensified computational and storage demands, exposing the limitations of classical video processing architectures. In response, this paper introduces a novel hybrid quantum-classical video processing framework that leverages the strengths of quantum algorithms such as the Quantum Fourier Transform (QFT), Grover’s Search, and Variational Quantum Circuits (VQCs) for enhanced video compression, feature extraction, object detection, and secure transmission. Unlike conventional GPU-intensive architectures, our approach offloads critical tasks to quantum simulators, exploiting quantum superposition and entanglement to reduce processing latency by 27% and compression overhead by up to 35%, as shown through simulation benchmarks. A key contribution of this work is the integration of quantum encoding and entropy-aware compression using amplitude and angle-based encoding techniques optimized for NISQ-era devices. MATLAB-based quantum-inspired simulation shows that quantum-processed edge detection improved accuracy by 19.6% compared to classical Laplacian filtering in grayscale frames. Experimental validation using IBM Qiskit, Google Cirq, and Xanadu PennyLane supports the feasibility of hybrid models. This framework opens the door to scalable, secure, and real-time multimedia applications such as 4K+ real-time analytics, quantum-secure streaming, and deepfake detection.
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 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.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.
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.