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.
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.
Parkinson's disease (PD) is a progressive neuro-degenerative condition with an urgent need for timely and pinpoint diagnosis for adequate treatment. Despite potential for prediction with traditional machine learning techniques, PD data remains difficult to solve for traditional machine learning techniques due to the high computational complexity and the nature of data. We introduce in this work a Hybrid Quantum-Classical AI Framework that combines advantages of QC and provide benefits in predictive accuracy as well as computational efficiency for real time Parkinson's disease detection. We use classical deep learning models in conjunction with Variational Quantum Circuits (VQC) to perform efficient feature extraction and classification of biomedical signals: speech and its recordings, gait analysis, and EEG data. We evaluate the performance of our proposed framework against deep learning models that are standard and based on the accuracy, precision, recall and processing time. Our experimental results suggest that our hybrid quantum classical model outperforms classical methods for both accuracy and efficiency, demonstrating the potential of quantum machine learning (QML) in healthcare. This work gives a taste of the quantum revolution realized in medical diagnostics and paves the way for what could in the future be quantum enhanced disease prediction.
BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.
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.
Abstract Parkinson’s disease (PD) involves a slowly advancing neurological condition that profoundly impairs motor as well as cognitive abilities, making prompt and precise diagnosis essential for successful treatment. Diagnosing PD using MRI images is difficult because of subtle changes within the brain and imbalanced datasets. While existing methods such as traditional Machine Learning (ML) and Deep Learning (DL) have made progress, they often struggle to capture important features and effectively handle limited data . To resolve these concerns, this work suggests a combined approach starting with generating additional MRI images using a Deep Convolutional Generative Adversarial Network (DCGAN) to balance the dataset. The feature extraction from these images is performed using the InceptionV3 model. These features are then enhanced by a Pyramid Attention Network (PAN), which helps to concentrate on the data’s most pertinent sections. Finally, the enhanced features are classified using a Variational Quantum Classifier (VQC) with amplitude encoding, which leverages quantum-inspired ML techniques to improve classification performance. This pipeline achieved an overall accuracy of 82.04%, outperforming earlier models. The results demonstrate the feasibility of integrating quantum-inspired models within a hybrid framework for PD classification from MRI scans.
A novel technique in early Parkinson's disease diagnosis with the use of a quantum long short-term memory model is described in this paper. Quantum computing, therefore, coupled with deep learning, seeks to improve the accuracy as well as efficiency of classifying traditional LSTM models, which even becomes more critical in cases of neurodegenerative diseases like Parkinson's disease, where diagnostics at an early stage would be helpful. The model was tested heavily on public datasets and compared with traditional deep learning models, which were significantly outperformed by this model with its superior accuracy and precision and recall rates. Furthermore, the computation time hugely became lower and thus possibly may revolutionize diagnostic procedures. These innovations will eventually establish quantum machine learning in medicine at a more accurate and faster diagnosis pace. Future development will focus on expanding the quantum LSTM so it can be more generally applicable to various situations for other medical conditions. Researchers believe that the configuration of the quantum layers should be modified, and this model will be extended to other deep learning models to have a more comprehensive and versatile tool in diagnosis for Parkinson's disease and perhaps other medical uses. The ultimate vision in the long term would be to use quantum-based models, not only for early detection but also improvement in prediction and personalization of treatment to revolutionize health care and thereby improve patient outcomes with neurodegenerative diseases.
The prompt detection of neurodegenerative diseases (NDDs) like Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) is essential yet difficult. This research proposes a hybrid quantum-classical machine learning methodology to categorize NDD using multimodal brain imaging data.ADNI and PPMI datasets' MRI, PET, and fMRI images were preprocessing by Python-based tools such as Scikit-image, SimpleITK, and FreeSurfer via Nipype. Major steps were denoising, registration, normalization, segmentation, and extraction of features from structural and functional imaging. Training was done with a Quantum Support Vector Machine (QSVM) using Qiskit's ZZFeatureMap and QuantumKernel and compared with a conventional SVM. Performance measures-precision, recall, F1-score, class accuracy, AUC, and 10-fold cross-validation-were calculated. QSVM performed better than the standard SVM on all metrics, with 91.25% overall accuracy and macro F1-score of 0.913 as opposed to 87.75% and 0.879 for the SVM. The results validate QSVM's advantage in handling complex, high-dimensional neuroimaging data and its relevance to aiding clinical diagnosis. Keywords: Quantum Machine Learning, Neuroimaging, QSVM, Alzheimer’s Disease, Parkinson’s Disease, Multimodal Classification