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.
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.
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.
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.
The problem of classifying brain tumours using magnetic resonance imaging (MRI) is a fundamental one in the field of computeraided diagnosis, since although many commonly employed convolutional neural network (CNN) architectures provide high accuracy they contain a large number of parameters, which has led to a search for more compact network designs. In this paper a hybrid quantum–classical approach is proposed that combines a lightweight CNN feature extractor with a trainable variational quantum circuit (VQC) in order to carry out four-class brain tumour classification. The MRI images are resized to 128 by 128 pixels and then processed by a small three-block CNN that reduces each image to an 8-dimensional feature vector. This vector is put into an 8-qubit quantum register by means of angle embedding and then acted on by a variational circuit composed of strongly entangling layers, by measuring each qubit in the Pauli-Z basis. The quantum features thus obtained are then fed into classical dense layers that categorise the images as glioma, meningioma, pituitary tumour or no tumour. The framework was tested on the Brain Tumor MRI dataset (7,200 images) and compared with a classical CNN baseline that was trained under the same conditions. The hybrid model reached a test accuracy of 76.88%, as compared to 72.31% for the classical baseline an improvement of 4.57 percentage points together with corresponding increases in macro-averaged precision, recall and F1-score. Notably, the hybrid model needed only 72 additional trainable parameters when compared to the classical baseline. The fact that the quantum layer was assessed using a noiseless simulator rather than on actual quantum hardware means that these results should be seen as indicating the feasibility of using compact quantum feature transformations for medical image classification rather than as showing a quantum advantage.
Brain tumor image classification is an essential pre-processing task for tumor diagnosis and treatment planning. In medical imaging, quantum computing analyse medical imaging data, offering potential speedups and new representations compared to classical methods. The brain images are transformed through resizing and normalization to a uniform 32 × 32 × 3 resolution. Subsequently, these processed images are converted into vectors to enable encoding into quantum states. The feature set is reduced using Principal Component Analysis (PCA) to ensure compatibility with the restricted qubit count in the quantum circuits. Through amplitude encoding, these vectors are mapped to quantum states, allowing for a compact and efficient data representation within the quantum domain. A parameterized Variational Quantum Circuit(VQC), consisting of entangling gates and trainable rotations, processes the encoded quantum states to learn features that can effectively discriminate between classes. The loss is calculated from the measured expectation values of Pauli-Z observables and optimized using classical gradient descent, where the objective function is the mean squared error. The trained quantum model is designed for multi-class classification, specifically distinguishing between meningioma, glioma tumors, pituitary tumors, and the absence of a tumor. The outcome of this proposed work demonstrates the VQC’s advantage over classical baselines, including Random Forest, and Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The VQC achieved the highest accuracy of 95% and the best robustness (Specificity 0.983). Crucially, the VQC accomplished this using only 24 trainable parameters, showcasing a significant increase in parameter efficiency compared to the classical models. The novelty of this work is the quantitative demonstration that a custom-designed, resource-efficient quantum model can outperform established classical deep learning methods in both performance and efficiency for complex medical image classification. The VQC provides a 3.5-fold gain in parameter efficiency, achieving the highest accuracy of 95% using only 24 trainable parameters. This efficiency is enabled by the novel application of amplitude encoding to the medical images, which compresses 16 features into just 4 qubits. The VQC demonstrates robustness vital for medical diagnostics, achieving a Specificity of 0.983. Its shallow architecture is also NISQ-friendly and remains stable by avoiding the Barren Plateau issue (Gradient Norm: 0.11), validating the path toward quantum advantage in diagnostic tools. The results of this approach highlight the potential amplitude encoding in quantum machine learning, as a feasible and effective method for classifying real-world medical images. This work also opens avenues for future hybrid quantum-classical diagnostic systems that could utilize richer quantum state encodings for significant gains in computational efficiency and accuracy.
Abstract Quantum machine learning (QML) has recently been explored as a means of augmenting classical convolutional neural networks (CNNs) for medical image analysis, yet most existing hybrid architectures insert a single quantum component either at the input or the output of an otherwise classical pipeline, and few works empirically isolate which placement is actually responsible for performance change. This paper proposes QCQ-CNN, a Quantum-Classical-Quantum hybrid framework for brain tumor magnetic resonance imaging (MRI) classification in which a quantum convolutional (“quanvolutional”) layer performs early-stage feature extraction, a classical CNN backbone performs hierarchical representation learning, and a variational quantum circuit (VQC) performs final classification, so that quantum processing brackets the classical backbone at both ends. Using a real, held-out four-class brain tumor MRI dataset (glioma, meningioma, pituitary tumor, and no tumor), we implement and train four architectural variants under matched conditions — (A) a fully classical CNN baseline, (B) a quantum-convolution-plus-classical-CNN variant, (C) a classical-CNN-plus-quantum-head variant, and (D) the full proposed QCQ-CNN — and report actual experimental accuracy, precision, recall, and F1-score on a common test set, together with training curves, confusion matrices, and qualitative visualizations of the learned quantum feature maps. Our small-scale experiments show that quantum convolutional preprocessing alone improves macro-F1 over the classical baseline (0.204 to 0.382), while replacing the classical classification head with a variational quantum circuit degrades performance in both the quantum-head-only and the full QCQ-CNN configurations. We discuss this finding in the context of variational quantum circuit trainability at low qubit counts and limited data, and argue that our staged ablation methodology — rather than a single best-accuracy claim — constitutes the paper's principal novelty: a reusable protocol for empirically localizing where, in a CNN pipeline, quantum layers help versus hurt. We conclude with concrete design recommendations and directions for scaling QCQ-style frameworks. Keywords: Quantum Machine Learning, Hybrid Quantum-Classical Neural Networks, Quanvolutional Neural Network, Variational Quantum Circuit, Convolutional Neural Network, Brain Tumor Classification, Medical Image Processing
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.