Parkinson Disease is a progressive neurodegenerative disorder for which accessible, low-cost screening methods remain limited. Sustained-vowel voice recordings contain measurable acoustic biomarkers associated with disease-related dysphonia that can be acquired without specialized equipment. This protocol describes a four-qubit quantum-classical hybrid convolutional neural network (QI-HCNN) that combines a parameterized quantum circuit with a shallow classical classification layer to classify Parkinson Disease using dimensionally reduced acoustic voice features. The protocol also benchmarks the proposed approach against a dimensionality-matched classical neural network and gradient-boosted tree classifiers trained on both the reduced and full feature sets. Using a publicly available, de-identified dataset comprising 195 voice recordings from 31 individuals, the QI-HCNN achieved an area under the receiver operating characteristic curve of 0.78 using three-fold stratified cross-validation, compared with 0.87 for the matched classical neural network and 0.94-0.95 for the gradient-boosted baselines trained on the reduced and full feature sets, respectively. A controlled ablation analysis demonstrated that one of the two entanglement operations in the circuit could not influence the measured output by design, whereas the remaining entanglement operation reduced, rather than improved, classification performance relative to a non-entangled variant. Patient-grouped cross-validation further showed that performance estimates varied substantially depending on the individuals assigned to the test set, reflecting the limited cohort size. This protocol therefore provides a fully specified and independently reproducible quantum-classical baseline together with a data-supported evaluation of the strengths and limitations of the current circuit design, providing a methodological foundation for future investigations rather than supporting claims of diagnostic readiness.
Parkinson Disease is a progressive neurodegenerative disorder for which accessible, low-cost screening methods remain limited. Sustained-vowel voice recordings contain measurable acoustic biomarkers associated with disease-related dysphonia that can be acquired without specialized equipment. This protocol describes a four-qubit quantum-classical hybrid convolutional neural network (QI-HCNN) that combines a parameterized quantum circuit with a shallow classical classification layer to classify Parkinson Disease using dimensionally reduced acoustic voice features. The protocol also benchmarks the proposed approach against a dimensionality-matched classical neural network and gradient-boosted tree classifiers trained on both the reduced and full feature sets. Using a publicly available, de-identified dataset comprising 195 voice recordings from 31 individuals, the QI-HCNN achieved an area under the receiver operating characteristic curve of 0.78 using three-fold stratified cross-validation, compared with 0.87 for the matched classical neural network and 0.94-0.95 for the gradient-boosted baselines trained on the reduced and full feature sets, respectively. A controlled ablation analysis demonstrated that one of the two entanglement operations in the circuit could not influence the measured output by design, whereas the remaining entanglement operation reduced, rather than improved, classification performance relative to a non-entangled variant. Patient-grouped cross-validation further showed that performance estimates varied substantially depending on the individuals assigned to the test set, reflecting the limited cohort size. This protocol therefore provides a fully specified and independently reproducible quantum-classical baseline together with a data-supported evaluation of the strengths and limitations of the current circuit design, providing a methodological foundation for future investigations rather than supporting claims of diagnostic readiness.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines ( p = 0.031), while other quantum models do not achieve statistical significance over XGBoost ( p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
Introduction: Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, presenting with motor and non-motor symptoms in roughly 2-3% of the global population above age 60. Early detection is difficult because symptoms are subtle and often indistinguishable from normal aging. The MDS-UPDRS rating scale, the current clinical standard, is time-intensive, subjective, and requires experienced clinicians. Most computational approaches are unimodal and do not use image and structured clinical data in combination. Methods: We propose a hybrid quantum-classical dual-track multimodal network that classifies PD patients and healthy controls from hand-drawn spiral and meander patterns. The first track, the Topological Visual-Spatial Feature Encoder Network (TVSFE), uses a ghost module-based CNN with Cross-Dimensional Attention Bottleneck (CDAB) blocks incorporating coordinate attention, squeeze-and-excitation, and triplet attention, followed by a quantum variational circuit with amplitude embedding. The second track, the Variational Quantum Feature Mapping Network (VQFMN), encodes structured clinical and demographic data through RY rotation gates and strongly entangling layers. Outputs from both tracks are concatenated and passed through fully connected layers for classification. Results: On the HandPD test set, the model achieved 97.28% accuracy, 96.60% precision, 96.62% recall, and 96.54% F1-score, outperforming all CNN, transformer, and ML-based baselines compared. Five-fold cross-validation produced a mean accuracy of 96.58%. On the NewHandPD dataset, accuracy, precision, recall, and F1-score were all 95.45%. Discussion: The quantum-classical fusion outperforms both single-modality and fully classical variants. Grad-CAM localizes the spatial image regions driving classification and the perturbation-based sensitivity analysis identifies Root Mean Square (RMS) and age as the most influential structured features. Both together make the model's reasoning traceable at the modality level, which is important for decision-making.
Parkinson's disease is a progressive neurological disorder.Speech, gait, and handwriting often change before a specialist can confirm the diagnosis.Clinics still depend heavily on observation, which takes time and can vary between doctors.Recent papers combine machine learning with quantum and hybrid quantum-classical models to improve screening from multimodal data.This review covers eight studies (2023-2026) that use Quantum LSTM, variational quantum circuits, quantum neural networks, quantum-inspired SVM, and metaheuristic feature selection for PD detection.We note which biomarkers each work uses, the accuracy or AUC reported, and repeated limits: simulation-only runs, public datasets, and weak clinical follow-up.A shared pipeline is summarised.Quantum AI may help earlier detection, but surveyed systems are not yet ready for routine hospital use without hardware noise tests and longer patient studies.
Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.
Dysphonia, a prevalent medical condition, leads to voice loss, hoarseness, or speech interruptions. To assess it, researchers have been investigating various machine learning techniques alongside traditional medical assessments. Convolutional Neural Networks (CNNs) have gained popularity for their success in audio classification and speech recognition. However, the limited availability of speech data, poses a challenge for CNNs. This study compares the performance of CNNs with standard convolutional layers, CNNs with random nonlinear convolutional layers (RANDOM models), and a novel hybrid quantum-classical architecture, Quanvolutional Neural Networks (QNNs), which are well-suited for small datasets. The audio data was preprocessed into Mel spectrograms, comprising 243 training samples and 61 testing samples in total, and used in four experiments. A total of six models were developed: two CNNs, two RANDOM models, and two QNNs, with the second models incorporating additional layers to boost performance. The angle encoding method was employed within the quanvolutional layer, which is the quantum transformation layer of the QNNs, to enable the representation of more complex features of Mel spectrogram. The experimental results demonstrated that QNN models consistently outperformed the CNN models in both classification accuracy (ranging from 82.22% ± 4% to 89.26% ± 2%) and convergence speed across all four experiments.Clinical relevance- This work investigates the potential of quantum-based approaches for medical data classification and their promising role in enhancing dysphonia assessment.
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.