Abstract Brain tumor diagnosis from MRI remains a critical challenge in neuro-oncology, requiring accurate detection of subtle morphological differences across tumor types. Recent advances in quantum machine learning (QML) have introduced new possibilities for representing high-dimensional medical imaging data through quantum states, yet most existing models lack mechanisms to efficiently capture non-local spatial hierarchies and fine-grained structural variations essential for distinguishing glioma subtypes. To address these limitations, we present QuMo, a Quantum Moiré-based classifier that integrates quantum-enhanced feature extraction with Moiré pattern-inspired modulation kernels. Unlike conventional CNNs or standard variational quantum classifiers, our approach embeds spatial interference patterns directly into the quantum feature mapping pipeline, enabling compact yet expressive representations. We implement QuMo within a hybrid quantum–classical architecture using parameterized quantum circuits (PQCs) optimized by variational algorithms, and evaluate it on the BraTS brain tumor dataset. To ensure rigorous and reproducible comparisons, we benchmarked QuMo using the QUARK framework, a standardized benchmarking suite for quantum applications. QuMo was evaluated against a range of quantum and hybrid baselines, including PQC, VQC, QNN, QSVM, QBM, QCNN, and QFT-based classifiers, with metrics aggregated over multiple independent runs. Our results show that QuMo achieves higher classification accuracy, improved parameter efficiency, and reduced quantum resource requirements compared to all baselines. Benchmarking further demonstrates that QuMo exhibits lower variance and greater robustness across repeated trials. Moiré-based quantum kernels enhance sensitivity to heterogeneous tumor boundaries and subtle structural distortions, offering interpretable, scalable, and resource-efficient QML models for precision neurodiagnostics. This establishes geometric interference–driven quantum encodings as a promising direction for nextgeneration medical imaging analytics. The code will be publicly available at https://github.com/akortheanchor/QuMo.
Abstract Brain tumor diagnosis from MRI remains a critical challenge in neuro-oncology, requiring accurate detection of subtle morphological differences across tumor types. Recent advances in quantum machine learning (QML) have introduced new possibilities for representing high-dimensional medical imaging data through quantum states, yet most existing models lack mechanisms to efficiently capture non-local spatial hierarchies and fine-grained structural variations essential for distinguishing glioma subtypes. To address these limitations, we present QuMo, a Quantum Moiré-based classifier that integrates quantum-enhanced feature extraction with Moiré pattern-inspired modulation kernels. Unlike conventional CNNs or standard variational quantum classifiers, our approach embeds spatial interference patterns directly into the quantum feature mapping pipeline, enabling compact yet expressive representations. We implement QuMo within a hybrid quantum–classical architecture using parameterized quantum circuits (PQCs) optimized by variational algorithms, and evaluate it on the BraTS brain tumor dataset. To ensure rigorous and reproducible comparisons, we benchmarked QuMo using the QUARK framework, a standardized benchmarking suite for quantum applications. QuMo was evaluated against a range of quantum and hybrid baselines, including PQC, VQC, QNN, QSVM, QBM, QCNN, and QFT-based classifiers, with metrics aggregated over multiple independent runs. Our results show that QuMo achieves higher classification accuracy, improved parameter efficiency, and reduced quantum resource requirements compared to all baselines. Benchmarking further demonstrates that QuMo exhibits lower variance and greater robustness across repeated trials. Moiré-based quantum kernels enhance sensitivity to heterogeneous tumor boundaries and subtle structural distortions, offering interpretable, scalable, and resource-efficient QML models for precision neurodiagnostics. This establishes geometric interference–driven quantum encodings as a promising direction for nextgeneration medical imaging analytics. The code will be publicly available at https://github.com/akortheanchor/QuMo.
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.
Brain tumor classification from magnetic resonance imaging requires discriminating subtle morphological differences that confound both radiologists and automated pipelines.Existing quantum machine learning architectures for medical imaging provide no mechanism to preserve non-local spatial structure during feature encoding, discarding the inter-region phase relationships and boundary periodicity that separate tumor subtypes.A six-qubit hybrid quantum-classical architecture, QuMo, is introduced in which Moiré beat-field geometry is injected into the amplitude-encoding pipeline before variational optimization.A depthwise-separable convolutional encoder extracts spatial feature maps; the Moiré-Modulation (MoMo) operator applies frequency-controlled multiplicative interference to yield geometrically expressive, sparse amplitude vectors; and a variational quantum circuit trained by simultaneous perturbation stochastic approximation learns a task-specific unitary.Over five seeded trials on the BraTS and Kaggle brain tumor benchmarks, QuMo reaches 92 .5% ± 0 .6% accuracy at 62 total gate layers, a 13 .9% depth and 22 .2% CNOT reduction relative to the raw-encoding variational baseline, and records the highest accuracy and lowest crosstrial variance among seven quantum reference architectures.Inter-class Euclidean separation is verified independently of classifier output to increase by at least 1 .18 × across 5432 cross-class feature pairs.MoMo satisfies all three conditions of the Geometry-Driven Implementation Flexibility framework, improving seven downstream classical, hybrid and quantum classifiers.
Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high-dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12%, 93.30%, and 96.42%; F1-scores of 93.30% and 96.42%; and AUCs of 96.42% and 96.42%, respectively, outperforming conventional CNN, DNN, and SVM baselines. Ablation studies provide additional evidence of the performance improvements enabled by quantum Fourier transform and entanglement layers. Such results suggest that quantum–classical learning can efficiently improve feature extraction and discrimination in medical imaging, thus providing a modular and scalable route towards quantum–inspired clinical decision–support systems of the future.
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.
ABSTRACT Brain tumor classification from magnetic resonance imaging requires discriminating subtle morphological differences that confound both radiologists and automated pipelines. Existing quantum machine learning architectures for medical imaging provide no mechanism to preserve non‐local spatial structure during feature encoding, discarding the inter‐region phase relationships and boundary periodicity that separate tumor subtypes. A six‐qubit hybrid quantum–classical architecture, QuMo, is introduced in which Moiré beat‐field geometry is injected into the amplitude‐encoding pipeline before variational optimization. A depthwise‐separable convolutional encoder extracts spatial feature maps; the Moiré‐Modulation (MoMo) operator applies frequency‐controlled multiplicative interference to yield geometrically expressive, sparse amplitude vectors; and a variational quantum circuit trained by simultaneous perturbation stochastic approximation learns a task‐specific unitary. Over five seeded trials on the BraTS and Kaggle brain tumor benchmarks, QuMo reaches accuracy at 62 total gate layers, a depth and CNOT reduction relative to the raw‐encoding variational baseline, and records the highest accuracy and lowest cross‐trial variance among seven quantum reference architectures. Inter‐class Euclidean separation is verified independently of classifier output to increase by at least across 5432 cross‐class feature pairs. MoMo satisfies all three conditions of the Geometry‐Driven Implementation Flexibility framework, improving seven downstream classical, hybrid and quantum classifiers.
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.