Image segmentation represents a fundamental computer vision task, yet classical Fully Convolutional Networks (FCNs) often struggle to capture long-range dependencies and global context due to the limited receptive fields of local convolution operations. While incorporating fully connected layers at the bottleneck can address this limitation by enabling global information mixing, it introduces a prohibitive quadratic parameter growth scaling with the spatial resolution of the bottleneck feature map. We propose a hybrid quantum-classical UNet architecture that replaces the bottleneck layer with an eight-qubit variational quantum circuit, achieving constant parameter count regardless of input resolution while maintaining competitive segmentation performance. Through comprehensive experiments on synthetic segmentation tasks and validation on real-world ISIC 2016 medical lesion images, we demonstrate that quantum bottleneck layers achieve intersection-over-union of 0.8544 on synthetic data and 0.8644 on authentic medical imaging with expert annotations, representing parameter compression ratio of sixty-four fold compared to classical dense bottlenecks at 512×512 resolution. Theoretical kernel execution time analysis based on documented gate times suggests potential acceleration at standard resolutions, with asymptotic advantage growing as quantum hardware matures and overhead diminishes. Our theoretical analysis establishes that quantum circuits achieve logarithmic parameter scaling versus classical quadratic scaling for dense global context layers, with compression advantages growing exponentially as image resolution increases. Thiswork demonstrates the theoretical foundations for quantum-enhanced image segmentation with empirical validation on authentic clinical data, opening pathways for efficient deployment in resource-constrained medical imaging environments and high-resolution applications as quantum hardware infrastructure evolves.
Image segmentation represents a fundamental computer vision task, yet classical Fully Convolutional Networks (FCNs) often struggle to capture long-range dependencies and global context due to the limited receptive fields of local convolution operations. While incorporating fully connected layers at the bottleneck can address this limitation by enabling global information mixing, it introduces a prohibitive quadratic parameter growth scaling with the spatial resolution of the bottleneck feature map. We propose a hybrid quantum-classical UNet architecture that replaces the bottleneck layer with an eight-qubit variational quantum circuit, achieving constant parameter count regardless of input resolution while maintaining competitive segmentation performance. Through comprehensive experiments on synthetic segmentation tasks and validation on real-world ISIC 2016 medical lesion images, we demonstrate that quantum bottleneck layers achieve intersection-over-union of 0.8544 on synthetic data and 0.8644 on authentic medical imaging with expert annotations, representing parameter compression ratio of sixty-four fold compared to classical dense bottlenecks at 512×512 resolution. Theoretical kernel execution time analysis based on documented gate times suggests potential acceleration at standard resolutions, with asymptotic advantage growing as quantum hardware matures and overhead diminishes. Our theoretical analysis establishes that quantum circuits achieve logarithmic parameter scaling versus classical quadratic scaling for dense global context layers, with compression advantages growing exponentially as image resolution increases. Thiswork demonstrates the theoretical foundations for quantum-enhanced image segmentation with empirical validation on authentic clinical data, opening pathways for efficient deployment in resource-constrained medical imaging environments and high-resolution applications as quantum hardware infrastructure evolves.
Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML) provides an alternative representation paradigm by mapping classical features into quantum states, but its direct application to high-dimensional images remains challenging under near-term quantum hardware constraints. In this work, we propose HQ-UNet, a hybrid quantum-classical U-Net architecture that integrates a compact parameterized quantum circuit at the bottleneck of a classical U-Net. The proposed design uses a non-pooling quantum convolutional module to enrich highly compressed encoder features before decoding, while keeping the quantum component shallow and parameter-efficient. Experiments on the LandCover.ai dataset show that HQ-UNet achieves a mean IoU of 0.8050 and an overall accuracy of 94.76%, outperforming the classical U-Net baseline. These results suggest that compact quantum bottlenecks can enhance feature representation for remote sensing image segmentation under near-term quantum constraints. This highlights the potential of hybrid quantum-classical designs as a promising direction for parameter-efficient dense prediction in Earth observation.
Medical image classification has been a fundamental task in computer-aided diagnosis. In this work, we propose a hybrid quantum-classical deep learning framework that integrates a parameterized quantum layer into the ResNet-18 architecture for medical image classification. Our experiments are conducted on the OrganMNIST-Axial, OrganMNIST-Coronal, and OrganMNIST-Sagittal datasets from the MedMNIST v2 collection, each consisting of 28×28 grayscale images representing different organ sections. The quantum layer is implemented as a 6-qubit variational quantum circuit (VQC) that partially replaces the classical fully connected module, enabling the network to exploit quantum feature mapping and entanglement for enhanced nonlinear representation learning. This architectural modification reduces the number of classical trainable parameters by approximately 44% compared with the original ResNet-18 while preserving its representational capacity. We evaluate the proposed QResNet18 against classical baselines to investigate its potential advantages in terms of accuracy, robustness, and parameter efficiency. Experimental results demonstrate that the hybrid quantum-classical model achieves competitive or superior performance while maintaining a significantly reduced parameter count, highlighting the feasibility and promise of quantumenhanced medical image classification.
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.
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.
Abstract The U-Net architecture is widely recognized as one of the most prominent models for medicalimage segmentation, comprising an encoder and a decoder. The encoder is crucial for extractingimage features to enhance segmentation accuracy, typically incorporating convolutional and poolinglayers. However, standard encoder structures often miss some classical features and struggleto extract high-level features effectively. In this paper, a hybird Quantum-Classical Neural Network(QC-Net) model with a novel encoder is proposed, aiming to capture more representativefeatures. Our encoder features a Residual Convolutional Block (RCB) to primarily extract somemissed features, and then Efficient Channel Attention (ECA) is employed into the output featuremaps after the RCB and pooling operations to handle more complex and noisy information. Consequently,a two-qubit parameterized circuit is devised to capture the final output features of theencoder, aiming to further capture the hidden high-level features from the quantum dimension.The decoder incorporates a joint attention mechanism and deconvolution operations to recoverthe spatial resolution and detail information of the original input image. To validate its efficacy,we conduct skin lesion segmentation experiments utilizing the ISIC2018 dataset. Notably, ourQC-Net model outperforms both U-Net and CA-Net, achieving an average Dice coefficient of93.2%, indicating improvements of 5.43% and 1.12%, respectively. These results underscore theoutstanding performance of our proposed QC-Net model.