Early and accurate identification of autism spectrum disorder (ASD) remains a central challenge in clinical neuroscience due to the subjective nature and high resource demands of traditional diagnostic methods. This paper presents a hybrid classical–quantum learning framework for ASD detection, combining the representational power of quantum circuits with the reliability of classical machine learning. The proposed model, a Variational Quantum Classifier (VQC) implemented using PennyLane and PyTorch, processes structured behavioural and demographic screening data from child, adolescent, and adult cohorts. Features are classically preprocessed, encoded into quantum states through angle embedding, and refined using strongly entangling layers, before being post-processed through a classical dense network. Experimental results show perfect classification performance with 100% accuracy, precision, recall, and F1-score, outperforming comparable classical approaches. The training phase exhibited smooth convergence across 70 epochs, confirming model stability and learning efficiency. Although evaluated in simulation, this study establishes a proof-of-concept for leveraging quantum machine learning in medical diagnostics, laying groundwork for future integration with real quantum hardware in clinical screening applications.
Early and accurate identification of autism spectrum disorder (ASD) remains a central challenge in clinical neuroscience due to the subjective nature and high resource demands of traditional diagnostic methods. This paper presents a hybrid classical–quantum learning framework for ASD detection, combining the representational power of quantum circuits with the reliability of classical machine learning. The proposed model, a Variational Quantum Classifier (VQC) implemented using PennyLane and PyTorch, processes structured behavioural and demographic screening data from child, adolescent, and adult cohorts. Features are classically preprocessed, encoded into quantum states through angle embedding, and refined using strongly entangling layers, before being post-processed through a classical dense network. Experimental results show perfect classification performance with 100% accuracy, precision, recall, and F1-score, outperforming comparable classical approaches. The training phase exhibited smooth convergence across 70 epochs, confirming model stability and learning efficiency. Although evaluated in simulation, this study establishes a proof-of-concept for leveraging quantum machine learning in medical diagnostics, laying groundwork for future integration with real quantum hardware in clinical screening applications.
Early detection of Autism Spectrum Disorder (ASD) is crucial for initiating interventions that can significantly improve developmental outcomes. Traditional diagnostic methods are often subjective and time-consuming, leading to delays in diagnosis. While classical machine learning models have been applied to ASD detection with promising results, they face challenges in processing complex, high-dimensional medical data. Quantum computing utilizes superposition, and entanglement offers a novel approach to handle such data more efficiently. This study investigates the application of Quantum Support Vector Machines (QSVM) for ASD detection using the Autism Screening Data for Toddlers (ASDTests) dataset. After preprocessing and feature selection, we trained a QSVM model. We compared its performance with classical machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We evaluated the models based on accuracy, precision, recall, and F1-score across multiple random subsets to assess robustness. The QSVM model achieved the highest mean accuracy (96.7 %), precision (97.2 %), and F1-score (95.9 %) with low variance, indicating strong and consistent performance. Statistical analyses using the Paired t-test and Friedman Test showed marginal improvements over the SVM model (p = 0.0495), with no significant difference compared to RF and NB models. These results suggest that QSVM can enhance the accuracy and robustness of ASD detection compared to classical models. Future work could explore hybrid models combining quantum and classical machine learning approaches to improve performance. Applying these methods to different ASD datasets may provide broader validation and insights into their generalizability.
Autism spectrum disorder (ASD) remains one of the foremost clinical challenges for early detection, and subjective diagnostic protocols constrain utilization while classical computational methods have failed to decode subtle oculomotor biomarker signatures. This work asks whether quantum feature encoding can move oculomotor ASD screening beyond the representational ceiling reached by classical eye-tracking classifiers, and answers this with a Hybrid Quantum–Classical Learning (QHCL) framework that combines parameterized quantum circuits with classical deep learning architectures to extract, encode, and classify eye-tracking biomarkers for early ASD detection and digital phenotyping. This is achieved by encoding raw gaze signals — fixation durations, saccadic velocities, scan path geometries, and pupillary response dynamics — into quantum Hilbert space representations through amplitude embedding to enable simultaneous processing of high-dimensional spatiotemporal oculomotor patterns. Quantum kernel-based feature learning reveals latent inter-feature correlations elusive to standard algorithms, and an entanglement-driven circuit layer forms enhanced patient-specific gaze embeddings. These embeddings are passed into a classical dual-head classifier trained for binary ASD classification and continuous digital phenotype evaluation along severity spectra. On three benchmark paediatric eye-tracking datasets, the proposed framework achieves classification accuracies of 95.8%, 97.4% and 96.2%, outperforming the strongest available classical baseline by 1.3–3.5 percentage points and improving quantum-versus-PCA feature learning by up to 9.3% at the individual feature-family level. The research contribution of this work is twofold: (i) it is the first study to apply quantum amplitude encoding and entanglement-driven parameterised circuits to oculomotor biomarker analysis for ASD, establishing that quantum Hilbert-space representations capture inter-feature correlations among fixation, saccadic, scanpath and pupillary signals that classical PCA-based pipelines discard; and (ii) it introduces a shared dual-head quantum-classical architecture that jointly optimises binary detection and four-class severity phenotyping from a single embedding, narrowing the accuracy gap that typically separates these two tasks in the literature. These findings open a new, non-invasive and computationally efficient pathway toward precision ASD screening with clinical applicability.
Asthma remains a prevalent chronic respiratory disorder characterized by complex pathophysiological mechanisms and heterogeneous clinical manifestations. Accurate and timely diagnosis is critical for mitigating disease progression and optimizing patient outcomes. Conventional machine learning approaches often struggle with high-dimensional biomedical datasets, where redundant or irrelevant features can obscure discriminative patterns and degrade predictive performance. In this study, we propose a hybrid quantum-classical learning framework for asthma diagnosis that integrates Quantum Approximate Optimization Algorithm (QAOA)-based feature selection with a Variational Quantum Classifier (VQC) using angle embedding. The publicly available Asthma Disease Dataset from Kaggle, comprising demographic, lifestyle, environmental, and clinical variables, serves as the evaluation benchmark. The QAOA module exploits quantum parallelism, superposition, and entanglement to identify an optimal subset of predictive features, achieving an average dimensionality reduction of 73.3%. The selected features are subsequently encoded into quantum states and classified by a multi-layer VQC with chain entanglement. Simulations were conducted on a noiseless Pennylane default.qubit backend to assess the theoretical performance ceiling. Experimental results demonstrate that the proposed pipeline achieves an accuracy of 98.4%, surpassing classical baselines such as SVM, Random Forest, and MLP, while maintaining competitive precision, recall, and F1-scores. These findings underscore the potential of hybrid quantum-classical architectures for high-dimensional diagnostic tasks and lay the groundwork for future deployment of quantum-assisted clinical decision support systems in respiratory healthcare.
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.
Abstract Heart disease remains to be a primary cause of death globally, requiring precise and timely predictive methodologies. Traditional machine learning methods that depend exclusively on classical clinical variables frequently have restricted predictive accuracy and do not adequately use the intricate nonlinear interactions present in patient data. This work examines the effectiveness of hybrid quantum-classical models for predicting heart disease by combining classical clinical data with quantum-derived representations via Variational Quantum Classifier (VQC), Estimator, and Sampler models. Twelve hybrid configurations, integrating three quantum feature maps (Z, ZZ, Pauli) with four ansatz architectures (RealAmplitudes, TwoLocal, EfficientSU2, PauliTwoDesign), were assessed inside the PennyLane and Qiskit frameworks. The results indicate that the VQC model in PennyLane reached optimal performance with Z–TwoLocal and ZZ–PauliTwoDesign configurations, achieving accuracy and F1-scores of up to 0.95. The Estimator model consistently produced reliable predictions, with Pauli–EfficientSU2 and Z–TwoLocal attaining accuracies of up to 0.91 and F1-scores of 0.92 across several frameworks. The Sampler model demonstrated 1reasonable performance, with the Pauli–EfficientSU2 configuration achieving an accuracy of 0.65 and an F1-score of 0.73, underscoring the supplementary role of stochastic quantum sampling. The analysis of feature significance indicated that the quantum-derived feature provided the most robust predictive signal, exceeding classical variables like Age, Blood Pressure, and Cholesterol. These findings demonstrate that meticulous alignment of feature maps and ansatz circuits is essential for optimizing model expressivity, convergence, and predictive effectiveness. In summary, hybrid quantum-classical models, especially the Estimator and VQC architectures, significantly improve heart disease prediction, providing a feasible method for precision clinical decision support and potential incorporation into privacy-preserving, multi-institutional healthcare systems.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.