The digitization of histopathology slides into high-resolution Whole-Slide Images (WSIs) has created a computational bottleneck for classical image processing algorithms. Tasks such as feature extraction and tumor segmentation require immense classical memory and processing power. This paper explores the application of Quantum Image Processing (QIP) as a solution. By utilizing frameworks such as the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR), we theoretically demonstrate how multi-gigapixel tissue scans can be encoded into high-dimensional Hilbert spaces. We propose a hybrid quantum-classical architecture that performs quantum edge detection via the Quantum Fourier Transform (QFT), achieving an exponential reduction in computational time complexity compared to classical convolutional edge filters. The results establish a foundational methodology for integrating quantum algorithms into clinical digital pathology workflows.
The digitization of histopathology slides into high-resolution Whole-Slide Images (WSIs) has created a computational bottleneck for classical image processing algorithms. Tasks such as feature extraction and tumor segmentation require immense classical memory and processing power. This paper explores the application of Quantum Image Processing (QIP) as a solution. By utilizing frameworks such as the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR), we theoretically demonstrate how multi-gigapixel tissue scans can be encoded into high-dimensional Hilbert spaces. We propose a hybrid quantum-classical architecture that performs quantum edge detection via the Quantum Fourier Transform (QFT), achieving an exponential reduction in computational time complexity compared to classical convolutional edge filters. The results establish a foundational methodology for integrating quantum algorithms into clinical digital pathology workflows.
This paper investigates quantum image processing for histopathological cancer detection, focusing on enhanced feature extraction and improved classification. We propose a hybrid quantum-classical framework in which quantum preprocessing circuits, specifically, Quantum Hadamard Edge Detection (QHED) and the Quantum Fourier Transform (QFT), extract subtle spatial and spectral features from hematoxylin and eosin-stained whole-slide images. These quantum-enhanced features are then classified using deep neural networks such as ResNet-50, VGG-16, and UNet. Experiments on the PatchCamelyon dataset demonstrate that quantum-enhanced preprocessing increases sensitivity in detecting cancerous regions, while integrating hybrid preprocessing with an SVM classifier further improves classification accuracy. Although quantum noise remains a challenge, our results highlight the promise of quantum image pre-processing and feature extraction for advancing histopathological image classification.
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 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.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
The characterization of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and prognosis. But the analysis of brain tumors based on MRI is still difficult because of the significant intra-tumoral heterogeneity, ambiguous boundaries of lesions, high dimensionality of imaging features, and complicated nonlinear relationships between anatomical structures. While transformer-based deep learning (DL) models have greatly advanced automated tumor analysis, classical feature representations often fail to effectively capture complex feature interactions in pathological images, especially when distinguishing between subtle pathological variations and normal tissue needs to be done efficiently. Recent quantum machine learning (QML) developments indicate that quantum feature encoding can map complex data to exponentially larger Hilbert spaces, leading to a more expressive representation of nonlinear patterns and feature correlations that are challenging to model with conventional architectures. Encouraged by this potential, this study proposes a hybrid quantum-classical framework, named QFormer-Brain, which combines transformer-based segmentation with quantum representation learning for automated brain tumor segmentation and classification. To segment the tumor regions, a Shifted Window UNet Transformers (Swin-UNETR) architecture is first used to capture both local anatomical details and global contextual information through hierarchical self-attention. Then, the deep semantic features of the segmented lesions are mapped to the quantum feature mapping module with angle encoding and variational quantum circuits (VQCs), which can obtain richer nonlinear feature representation by quantum superposition and entanglement mechanisms. The obtained quantum embeddings are then combined with features obtained from the transformer and classified by a Quantum Transformer Classifier. The experiments were performed on the publicly available BraTS 2021 data set using an 8-qubit variational quantum circuit, with 1024 measurement shots and readout-error mitigation, via the IBM Qiskit Aer simulator. The proposed framework achieved a Dice Similarity Coefficient (DSC) of 97.1%, Intersection-over-Union (IoU) of 95.0%, classification accuracy of 98.5%, sensitivity of 98.1%, specificity of 99.0%, and F1-score of 98.3%, outperforming U-Net, nnU-Net, ResNet50, Vision Transformer (ViT), and conventional hybrid Convolutional Neural Network (CNN)-based models. These results show that quantum-inspired representation learning can be efficiently integrated into transformer-based models to boost the discriminative feature extraction from complex MRI data.
Quantum image processing is a research field that explores the use of quantum computing and algorithms for image processing tasks such as image encoding and edge detection. Although classical edge detection algorithms perform reasonably well and are quite efficient, they become outright slower when it comes to large datasets with high-resolution images. Quantum computing promises to deliver a significant performance boost and breakthroughs in various sectors. Quantum Hadamard Edge Detection (QHED) algorithm, for example, works at constant time complexity, and thus detects edges much faster than any classical algorithm. However, the original QHED algorithm is designed for Quantum Probability Image Encoding (QPIE) and mainly works for binary images. This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.