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.
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.
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.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detection but is plagued by high dimensionality and noise that complicates automated analysis. This article introduces a hybrid Transformer–Quantum Neural Network (QNN) model that combines deep learning and quantum computing to enhance P300 targeted ASD responses from non-targeted responses. The model uses transformer capabilities captures cross-domain correlations and feature-level dependencies across heterogeneous feature sources and the quantum heads introduced via parameterized quantum circuits detects high-order, nonlinear relations in a Hilbert space via entanglement and superposition. Experimental results on the BCIAUT-P300 dataset better classical alternatives with higher diagnostic accuracy at higher accuracy (0.921) and F1-score (0.915). Statistical comparisons confirm that the hybrid approach yields significant improvements in discriminative reliability and diagnostic odds ratio, justifying quantum feature embedding’s expressivity and efficiency. The results bear witness to the feasibility and potential of hybrid quantum–classical systems as potential future intelligent diagnostic devices for ASD responses from EEG signals.
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
Cyberbullying has become much more common due to the proliferation of social media, which has devastating effects on users' mental health and relationships. Automatic identification of abusive textual content has therefore become an important research problem. Cyberbullying detection has seen extensive use of classical machine learning approaches, notably Support Vector Machines (SVMs); however, their effectiveness can be limited when handling high-dimensional and context-rich textual features. Recent developments in quantum computing introduce new computational paradigms that may enable more expressive feature representations through quantum-enhanced learning models. In this study, we explore the potential of a hybrid. Nowadays classical-quantum architecture to apply a Quantum Support Vector Machine (QSVM) to detect cyberbullying. Experiments were performed on a publicly available Kaggle dataset containing 35,824 labeled tweets. Before TF-IDF feature extraction, the text data underwent preprocessing via cleaning and tokenization. Dimension reduction was carried out using Principal Component Analysis (PCA) in respect of qubit constraints. Then, they were embedded into quantum states using angle embedding. The accuracy, precision, recall, and F1-score measures were used to compare QSVM models with various qubit configurations (2, 4, and 6), as well as a traditional SVM baseline, under an 80-20 stratified train-test split. The findings indicate that the quality of QSVM performance is highest with the increase in qubit numbers and the maximum accuracy of the quantum computing method is 60 percent according to six qubits, whereas the classical SVM achieves 67.5 percent. Although the quantum model currently incurs higher computational overhead due to simulator-based implementation, increasing qubit dimensionality enhances its ability to capture complex and non-linear linguistic patterns. These observations indicate that QSVM-based cyberbullying detection remains exploratory at present, with scope for further improvement as quantum hardware and algorithms advance.
Abstract In this study, we compare the performance of classical Support Vector Machines (SVM) and Quantum Support Vector Machines (QSVM) on binary classification tasks, using 149 balanced datasets derived from a lung cancer diagnosis dataset. Each dataset consists of 120 samples and 12 features. Our primary objective is to evaluate whether QSVM provides measurable advantages over SVM, particularly in low-performing scenarios. To this end, we identified the 15 datasets where classical SVM exhibited the lowest F1-scores and conducted a focused comparative analysis. Results show that, in these challenging cases, QSVM achieved an average recall improvement of 99% over SVM, without compromising precision. This substantial gain in recall also led to a corresponding 48% increase in F1-score, on average. Statistical analysis using a normality test followed by a paired t-test confirmed the significance of these results. These findings suggest that QSVM can serve as a valuable alternative in situations where classical models struggle, especially when high recall is critical for early cancer detection.
This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.