Exploring Hybrid Quantum-Classical Machine Learning for Respiratory Sound Analysis
Diego García-Vega, Samuel González-Castillo, Francisco David González Martínez, Jaime García-Martínez, Elías F. Combarro, Francisco Jesus Canadas-Quesada, José Ranilla
Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust capabilities of classical learning algorithms with the potential of quantum variational circuits. To accomplish this, we undertake a comparative analysis between Quantum Support Vector Machines and Quantum Neural Networks, juxtaposed with the equivalent classical AI models, employing various signal representation techniques. Based on the experimental results, integrating quantum techniques into the analysis of respiratory sounds enhances the effectiveness of classical methods, particularly in scenarios with limited examples. This is a common scenario in this context, where data availability is typically constrained.
Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust capabilities of classical learning algorithms with the potential of quantum variational circuits. To accomplish this, we undertake a comparative analysis between Quantum Support Vector Machines and Quantum Neural Networks, juxtaposed with the equivalent classical AI models, employing various signal representation techniques. Based on the experimental results, integrating quantum techniques into the analysis of respiratory sounds enhances the effectiveness of classical methods, particularly in scenarios with limited examples. This is a common scenario in this context, where data availability is typically constrained.
Cardiovascular diseases (CVD) remain a leading cause of morbidity and mortality worldwide, with arrhythmia being one of their common manifestations. Traditional diagnostic methods, such as electrocardiography (ECG), rely heavily on expert interpretation for detecting irregular heart rhythms, which can lead to inaccuracies and delays in diagnosis, particularly in resource-limited settings. In recent years, machine learning (ML) has emerged as a powerful tool for improving the accuracy and efficiency of medical diagnostics. Although deep learning models have demonstrated remarkable success in various domains, their computational complexity poses challenges for real-time applications. This study investigates various quantum-classical hybrid algorithms for improving ECG-based arrhythmia classification. In this study, we employed three hybrid quantum-classical algorithms for arrhythmia detection, estimator-based Quantum Neural Network (QNN), sampler-based QNN, and Quantum Support Vector Classifier (QSVC), to enhance the accuracy of automated arrhythmia classification. The models are evaluated using the MIT-BIH arrhythmia database. The experimental results demonstrated that these three quantum models outperform traditional Recurrent Neural Networks (RNNs) and their variants in several metrics, including accuracy, sensitivity, and specificity. The QSVC (k = 16) model achieved the highest performance, with an accuracy of 97.7%, F1-score of 97.5%, and area under the receiver operating characteristic curve of 99.4%. Additionally, we developed an interactive application using StreamLit for real-time arrhythmia risk prediction and visual feedback. Overall, the proposed quantum ML model demonstrates significant potential for arrhythmia detection with robust and highly accurate performance, providing a solid foundation for future clinical deployment and integration into wearable devices.
Hybrid quantum-classical machine learning (HQML) models represent an emerging computational paradigm that combines the strengths of quantum computing and classical artificial intelligence to solve complex learning problems more efficiently.Quantum computing offers massive parallelism, superposition, and entanglement, enabling computational capabilities beyond classical systems for selected tasks.However, current quantum hardware remains limited by noise, decoherence, and scalability constraints.Hybrid architectures overcome these limitations by integrating quantum processors with classical machine learning frameworks, allowing practical implementation on Noisy Intermediate-Scale Quantum (NISQ) devices.This review paper presents a comprehensive study of hybrid quantum-classical machine learning models, including their architecture, working principles, quantum circuits, optimization techniques, and applications in healthcare, cybersecurity, finance, robotics, and big data analytics.The paper also discusses quantum neural networks, variational quantum circuits, quantum support vector machines, and quantum generative models.Furthermore, comparative analysis between classical and hybrid quantum systems is presented to evaluate performance, scalability, computational efficiency, and implementation challenges.Major issues such as quantum noise, hardware limitations, algorithm complexity, and high operational cost are examined in detail.Finally, future research directions and emerging trends in hybrid quantum-classical computing are discussed.The review concludes that HQML models have the potential to revolutionize next-generation intelligent computing systems.
The healthcare systems produce a tremendous volume of medical data which may be in the form of patient records, laboratory test results and diagnostic reports. It is quite necessary to analyze such data accurately and efficiently in order to detect diseases early and plan appropriate treatment . Conventional machine learning methods have a tendency to struggling with large-dimensional and complicated medical data. To address these shortcomings, this project suggests a hybrid Quantum Machine Learning (QML) scheme of analyzing medical data and diagnosing a disease. The suggested system combines the classical data preprocessing with the quantum-enhanced data learning algorithms including Variational Quantum Circuits (VQC) and Quantum Support Vector Machines (QSVM). Medical data is first processed by cleaning, normalizing and encoding to quantum states. Complex patterns and correlations that traditional algorithms can hardly be able to learn are then learned with the quantum model. The measures used to judge the performance are accuracy, precision, recall and F1-score. The results of the experimental process show that the model based on QML yields better diagnostic accuracy than the conventional machine learning. This is where there is great potential in real time medical decision support systems, with the future prediction of disease and the personalized solution of care. The suggested framework indicates the potential and benefits of switching to quantum computing methods in healthcare analytics.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).
Quantum machines enhance the capabilities of classical counterparts across various domains, notably in addressing real-world challenges.The classification of brain MR images for tumor detection is a crucial diagnostic process in the analysis of brain images.Traditional approaches, such as classical machine learning techniques and conventional deep learning structures like convolutional neural networks, are frequently employed for image classification.However, as the network size increases, training these models becomes increasingly arduous.Quantum algorithms offer advantages by optimizing the performance of classical algorithms through the incorporation of the intrinsic properties of quantum bits.In this paper, we proposed a hybrid classical and quantum convolutional neural network for Alzheimer's disease (AD) classification.The proposed model was further validated on the brain tumor classification task.The fundamental concept involves encoding data into quantum states, facilitating quicker information extraction, and subsequently utilizing this information to discern the data class.The proposed model results underscore the reliability and robustness and demonstrated by optimal performance accuracies across various datasets, the proposed model substantiates its efficacy in detecting and classifying AD disease and brain tumors.
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.