Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
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.
Computer vision is confronted with increasing computational bottlenecks and memory requirements hard to scale by normal convolutional neural networks. Quantum-inspired architectures are inspired by quantum mechanics, superposition, entanglement as well as interference to redesign visual information processing in a more efficient way. Quantum convolutional neural networks are one of the biggest improvements as they apply parameterized quantum circuits to explore several input states at a time. Quantum-inspired pruning algorithms have lowered the processing time, and quantum optical neural networks have used the wave nature of light to have parallel characteristics of features. Tensor network architectures are based on quantum physics and can be used to convert high-dimensional image data into information-rich elements and model multi-scale interactions. Hybrid quantum-classical systems can be described as using quantum-inspired processing and the reliability of established deep learning. Attention systems ensure that quantum circuits are dedicated to informative parts of an image, whereas knowledge transfer of classical models stabilizes quantum training. Nonetheless, basic constraints limit applications: the barren plateau problem, measurement overhead, preparation complexity of states and decoherence are all major challenges. The existing applications include quantum-inspired classical algorithms to physical hardware experiments. Thorough benchmarking demonstrates that the classical approaches can be much more effective than the quantum ones. Frameworks such as PennyLane extend accessibility, although best practice and careful consideration of the architecture involving the integration of quantum-inspired and classical computing is critical to realistic computer vision systems.
The field of quantum computing has seen significant progress in recent times, leading to the emergence of innovative methodologies across several areas, particularly in machine learning and neural networks. This research study introduces a quantum-inspired neural network (QINN) that has been particularly developed to improve image recognition capabilities. In contrast to conventional neural networks, the Quantum-inspired Neural Network (QINN) utilizes concepts derived from quantum mechanics, including superposition and entanglement, to process and encode information effectively. The paradigm we propose incorporates quantum bits (qubits) as computing units instead of traditional binary nodes, simultaneously enabling the representation of many states. Moreover, the use of the intrinsic parallelism of quantum mechanics is harnessed to provide expedited and more effective data processing, particularly within the concealed levels of the network. The researchers ran benchmark tests using well-recognized image datasets, including CIFAR-10 and ImageNet. The study's findings indicated that QINN displayed superior accuracy rates compared to traditional methods and revealed faster convergence throughout the training process. Additionally, our quantum-inspired methodology demonstrated significant efficacy in analyzing pictures containing nuanced characteristics, highlighting its potential in practical scenarios where precise identification is of utmost importance. In summary, integrating quantum principles with neural network topologies has the potential for groundbreaking advancement in image recognition skills. QINN is a groundbreaking advancement in this domain, emphasizing the unexplored capacity to use quantum phenomena for sophisticated machine learning objectives. Potential areas for future study are the expansion of network scalability to accommodate bigger datasets, exploration of security measures that are resilient against quantum threats, and investigating hybrid models that integrate both quantum and conventional paradigms to enhance overall performance.
In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of hybrid quantum-classical neural networks. These networks combine the power of classical neural networks with the computational advantages offered by quantum variational circuits. This study presents a comprehensive survey of hybrid quantum-classical machine learning algorithms, focusing on their architecture, performance, and hyperparameter optimization techniques. Introducing the concept of hybrid quantum-classical machine learning and its significance in contemporary image classification tasks. It explores the theoretical foundations of quantum variational circuits and their integration with classical neural network architectures. Special attention is given to prominent algorithms such as Quantum Convolutional Neural Networks, Quanvolutional Neural Networks, and Quantum ResNet, each offering unique advantages in image classification tasks. A major aspect covered in this study is the hyperparameter optimization techniques employed in hybrid quantum-classical machine learning. The chapter discusses quantum-inspired optimization methods tailored for efficiently tuning the hyperparameters of complex neural network models. Through comparative analysis, the effectiveness of these optimization techniques is demonstrated in reducing computational overhead and improving model performance. Moreover, the study also delves into experimental results obtained from benchmarking hybrid quantum-classical models against traditional classical approaches. It highlights the accuracy variations observed across different quantum architectures and explores the impact of architectural permutations on model convergence. Insights gained from these experiments provide valuable guidance for researchers and practitioners seeking to leverage hybrid quantum-classical neural networks in real-world applications.
Abstract Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)‐based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum‐Classical Convolutional Neural Network (HQC‐CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q‐Means++ Clustering for segmenting the images and a Max‐cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max‐cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology.