Lung cancer remains the leading cause of cancer-related mortality worldwide, with accurate subtype classification being critical for improving patient outcomes. Vision Transformers (ViTs) have recently advanced medical image analysis, but their integration with quantum computing remains underexplored, leaving a gap in leveraging quantum advantages for clinical applications. This study proposes a quantum-enhanced Vision Transformer (QViT) that embeds variational quantum circuits (VQCs) within attention and feed-forward layers to enrich feature learning. To assess robustness, two circuit families, Basic VQC and Quantum Approximate Optimization Algorithm (QAOA) based VQC, were evaluated across two depths, yielding four QViT configurations. The models were analyzed through cancer classification tasks, systematic quantum state tomography (QST) to examine quantum state properties, and noise resilience testing under realistic Noisy Intermediate-Scale Quantum (NISQ) conditions, reflecting current hardware limitations. On 3150 CT (Computed Tomography) images, the QViT-QAOA-D1 configuration achieved 98.52% accuracy with a training time of 674 s, outperforming other configurations. Quantum analyses further showed that Basic VQC provides stronger entanglement but requires deeper, less NISQ-friendly circuits, whereas QAOA maintains high-purity states at shallower depths. These results suggest that QViT provides a practical and scalable framework, where quantum-enhanced representations can improve diagnostic performance while remaining compatible with current hardware constraints. Future work will focus on validation using patient-level datasets and deployment on real quantum hardware to further assess clinical applicability and scalability.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with accurate subtype classification being critical for improving patient outcomes. Vision Transformers (ViTs) have recently advanced medical image analysis, but their integration with quantum computing remains underexplored, leaving a gap in leveraging quantum advantages for clinical applications. This study proposes a quantum-enhanced Vision Transformer (QViT) that embeds variational quantum circuits (VQCs) within attention and feed-forward layers to enrich feature learning. To assess robustness, two circuit families, Basic VQC and Quantum Approximate Optimization Algorithm (QAOA) based VQC, were evaluated across two depths, yielding four QViT configurations. The models were analyzed through cancer classification tasks, systematic quantum state tomography (QST) to examine quantum state properties, and noise resilience testing under realistic Noisy Intermediate-Scale Quantum (NISQ) conditions, reflecting current hardware limitations. On 3150 CT (Computed Tomography) images, the QViT-QAOA-D1 configuration achieved 98.52% accuracy with a training time of 674 s, outperforming other configurations. Quantum analyses further showed that Basic VQC provides stronger entanglement but requires deeper, less NISQ-friendly circuits, whereas QAOA maintains high-purity states at shallower depths. These results suggest that QViT provides a practical and scalable framework, where quantum-enhanced representations can improve diagnostic performance while remaining compatible with current hardware constraints. Future work will focus on validation using patient-level datasets and deployment on real quantum hardware to further assess clinical applicability and scalability.
Abstract This work presents a novel quantum-hybrid pipeline for lung nodule classification in computed tomography (CT) scans, combining vision transformer (ViT) embeddings with quantum orthogonal pyramidal circuits (QOPCs). The approach was evaluated on 681 lung nodule CT scans across axial, coronal, and sagittal planes. Two ViT configurations were tested: ViT 1 (1 head, 4 layers) and a Bayesian-optimized ViT 2 (4 heads, 8 layers). Features extracted from ViT embedding layers were reduced via principal component analysis to 2–16 dimensions and classified using the QOPC with reconfigurable beam splitter (RBS) gates. The proposed approach achieved unprecedented compression, up to 1,470 × × (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT 2 , k=8 k = 8 , h=8 h = 8 ) with only 46 trainable parameters and achieved computational efficiency (CE) up to 92.0. Training was accelerated up to 28 × × (0.030 vs. 0.833 min) while maintaining robust diagnostic performance (F1: 0.77−0.82, receiver operating characteristic area under the curve (ROC-AUC): 0.87−0.90). Ablation studies confirmed that the quantum layer outperforms conventional MLPs by +3.4% accuracy with 35% fewer parameters, while late fusion of multi-view predictions further improved performance to 85.4% accuracy and 0.92 ROC-AUC. These results establish hybrid ViT-QOPC architectures as a practical and resource-efficient framework for medical image analysis, demonstrating their ability to dramatically reduce computational cost without compromising clinical accuracy.
Introduction: Medical image classification is a crucial task in cancer diagnosis, relying on the accurate analysis of high-dimensional imaging data. While Convolutional Neural Networks (CNNs) have shown great success in this domain, their performance is often limited by the shallow feature expressiveness and overfitting, particularly in small or heterogeneous datasets. Methods: Quantum machine learning offers new opportunities through high-dimensional representations and nonlinear transformations. In this work, we propose a Quantum-Enhanced Sandwich Convolutional Neural Network (QSCNN), a layered hybrid architecture that integrates quantum and classical modules. The model employs a quanvolutional layer for localized quantum feature extraction, followed by conventional convolution and pooling for hierarchical representation learning, and a variational quantum classifier for nonlinear decision-making. Results: QSCNN achieved higher accuracy and training stability than classical CNNs and QCCNN baselines across three medical imaging tasks.: brain tumor MRI, skin cancer dermoscopy, and lung cancer CT. Circuit depth analysis revealed a trade-off between expressiveness and robustness, and additional experiments with depolarizing noise confirmed the model’s resilience under realistic quantum error conditions. Discussion: This suggests that circuit design choices influence hybrid model behavior and generalization, supporting the feasibility of quantum-enhanced methods for small-sample medical imaging. However, the current evaluation is limited to relatively small benchmark datasets, and broader validation on large-scale data will be essential to confirm clinical applicability. Conclusion: In summary, QSCNN presents a feasible hybrid framework for enhancing medical image classification with quantum features. While preliminary, our results suggest potential advantages in accuracy and stability under NISQ conditions.
Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum Circuits (VQCs) at their core and featuring layers of quantum gates optimized by classical algorithms, have garnered significant attention. However, a rigorous and exhaustive evaluation of their practical performance remains largely incomplete. In this study, we conduct a comprehensive comparative analysis of three prominent hybrid classical-quantum architectures: Quantum Convolutional Neural Networks (QCNN), Quantum Recurrent Neural Networks (QRNN), and Quantum Vision Transformers (QViT), focusing on the critical dimensions of generalization, accuracy, and robustness. Our findings provide novel insights that address previous evaluative gaps. Notably, while these models exhibit exceptional performance on low-feature datasets such as MNIST, their learning efficacy degrades significantly when transitioned to high-feature datasets. Furthermore, convolutional-based models like QCNN appear less effective on high-dimensional data than other machine learning architectures. Additionally, while all models are susceptible to adversarial noise, traditional architectures, such as recurrent and convolutional networks, demonstrate superior resilience. Conversely, in the presence of quantum noise, the transformer-based architecture proves its strength by maintaining high robustness against measurement noise, channel noise, and finite-shot effects, whereas other architectures suffer marked performance declines. These results provide a granular perspective on the current state of the field and underscore the critical importance of tailoring model selection to the constraints of contemporary Noisy Intermediate-Scale Quantum (NISQ) environments.
In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In particular, transformers for High Energy Physics (HEP) experiments continue to be investigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation. An improved Quantum Vision Transformer (QViT) with a quantum-enhanced self-attention mechanism is introduced and discussed. A shallow circuit is proposed for each component of self-attention to leverage current Noisy Intermediate Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed. The results demonstrate a successful proof of concept for the QViT, and establish a competitive performance benchmark for the proposed design and implementation. The findings also provide strong motivation to experiment with different architectures, hyperparameters, and datasets, setting the stage for implementation in HEP environments where transformers are increasingly used in state of the art machine learning solutions.
Although automated diagnosis in medical imaging has made tremendous breakthroughs with deep learning, current models also exhibit difficulties with representation stability, calibration, and robustness to domain heterogeneity. Traditional methods that use deeper CNN or transformer networks incur greater computational costs without fully addressing feature separability and uncertainty estimation. To address these shortcomings, the present study proposes a hybrid quantum-classical approach, VQMedNet, that integrates variational quantum circuits with classical feature extraction for supervised classification of MRI and CT images. The suggested QFeatEmbed-VQC module is an NISQ-conscious nonlinear quantum feature embedding that can be trained on tokenised and compressed image features, enabling effective learning with constrained quantum resources. A gradual training approach is followed to achieve consistent optimisation, and calibration-sensitive assessment is included to measure the predictability. The experiments with the brain tumour MRI (BraTS) and lung CT (LIDC-IDRI) datasets show a stable increase over the classical baseline, achieving 95.6% accuracy and 0.947 Macro-F1 on MRI classification, and 93.1% accuracy with improved AUC-PR on CT tasks. Additional analyses show enhanced embedding separability, robustness to domain perturbations, and reduced Expected Calibration Error. Moreover, the findings suggest that moderate-depth quantum circuits offer a good balance between performance and computational complexity. Although tested under simulation-based conditions, the proposed framework can indicate the potential of quantum-enhanced feature embedding for building stable and efficient medical AI systems. It may be seen as a promising approach to future NISQ-aware diagnostics. The implementation is publicly available at: https://github.com/gvvsrk/QuantumMed-FedDx . Clinical trial number: Not applicable.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.