Early detection of clinical deterioration is crucial for better patient outcomes, and current diagnostic models struggle to model the complex interactions observed in nonlinear patterns in multimodal medical data while meeting the performance requirements for real-time operation. This paper proposes a Real-Time Hybrid Quantum-Classical Diagnostic Framework (HQCDF) to improve early disease detection by combining a Quantum Variational Biomarker Embedding module (QVBE), a Classical Temporal-Clinical Learning module (CTCLM), and a novel Quantum-Classical Diagnostic Fusion mechanism (QCDF). A semi-synthetic multimodal dataset, Med-EarlyQ, physiological time series, lab biomarkers, and clinical scores was built to evaluate. The hybrid model has been trained using End-to-end differentiable quantum-classical optimization via the parameter-shift rule. Results showed that HQCDF obtained 96% accuracy, 0.93 F1-score, and 0.95 AUROC to outperform state-of-the-art classical deep learning models and existing hybrid approaches by 6~15% while controlling the real-time inference latency to 18 ms. Analysis revealed that the QCDF module contributed to increased robustness against quantum noise and missing clinical information, and to QE's increased sensitivity to subtle trends in disease in its early stages. The results show that the proposed hybrid framework is a promising evolution, addressing the call for next-generation, real-time, and resource-efficient diagnostic systems with great potential for early intervention and clinical decision-support impact.
Early detection of clinical deterioration is crucial for better patient outcomes, and current diagnostic models struggle to model the complex interactions observed in nonlinear patterns in multimodal medical data while meeting the performance requirements for real-time operation. This paper proposes a Real-Time Hybrid Quantum-Classical Diagnostic Framework (HQCDF) to improve early disease detection by combining a Quantum Variational Biomarker Embedding module (QVBE), a Classical Temporal-Clinical Learning module (CTCLM), and a novel Quantum-Classical Diagnostic Fusion mechanism (QCDF). A semi-synthetic multimodal dataset, Med-EarlyQ, physiological time series, lab biomarkers, and clinical scores was built to evaluate. The hybrid model has been trained using End-to-end differentiable quantum-classical optimization via the parameter-shift rule. Results showed that HQCDF obtained 96% accuracy, 0.93 F1-score, and 0.95 AUROC to outperform state-of-the-art classical deep learning models and existing hybrid approaches by 6~15% while controlling the real-time inference latency to 18 ms. Analysis revealed that the QCDF module contributed to increased robustness against quantum noise and missing clinical information, and to QE's increased sensitivity to subtle trends in disease in its early stages. The results show that the proposed hybrid framework is a promising evolution, addressing the call for next-generation, real-time, and resource-efficient diagnostic systems with great potential for early intervention and clinical decision-support impact.
Early and accurate recognition of cardiovascular disease (CVD) using medical imaging is essential for timely clinical intervention and management. The objective of this study was to continue developing a hybrid quantum-classical deep learning system for the early detection and automatic triage of cardiovascular diseases using cardiac imaging information. The proposed method consists of a combination of efficient feature extraction via a lightweight classical convolutional neural network and a quantum-illness feature transformation and decision-refinement process implemented as a variational quantum circuit. A clinically informed triage module was added to rate patients into three risk categories (low-, moderate-, and high-risk). Experimental evaluation on a public cardiac magnetic resonance imaging (MRI) dataset revealed that the proposed framework achieved 94.5% classification accuracy and an area under the curve (AUC) of 0.96, with sensitivity exceeding that of state-of-the-art classical deep learning models. The results show that quantum-enhanced feature representations provide better class separability, robustness to Imaging noise, and improved learning efficiency. The results suggest that one way to overcome the limitations of conventional deep models is the hybrid quantum-classical learning approach, which is also compatible with near-term quantum hardware. This work raises the question of whether quantum-assisted artificial intelligence can enable scalable, real-time cardiovascular decision support systems and promote next-generation intelligent healthcare systems.
Early and accurate identification of heart conditions from electrocardiogram (ECG) signals is particularly important for ongoing patient health monitoring, yet classical deep learning frameworks have been inadequate at achieving high accuracy and low latency under noisy, real-time conditions. This work focuses on developing a quantum-accelerated cardiac ECG (electrocardiogram) analysis. The study introduces a novel hybrid quantum–classical framework capable of simultaneously performing ECG denoising, feature embedding, and arrhythmia classification with reduced latency and improved robustness under noisy real-time conditions. The one introduced in the article is the hybrid quantum-classical architecture, including Quantum Variational ECG Embedding (QVEE) for high-dimensional morphological representation, Quantum Enhanced Denoising Module (QEDM) for noise suppression and signal distortion, and hybrid quantum classification for arrhythmia recognition. Experiments were conducted on the MIT-BIH Arrhythmia Database, and a corpus with noise augmentation showed that the proposed framework was found to be 99.4% correct with an F1 score of 0.97 and reduced the inference latency by 23.7% compared to state-of-the-art CNN-LSTM, Transformer-based models, and showed higher robustness under the condition of low signal-to-noise ratios. The results show that quantum embeddings tend to improve ECG feature separability and that quantum denoising helps preserve clinically relevant waveform structure (i.e., structure detection), particularly for rare arrhythmias. The proposed framework is a promising approach for establishing real-time quantum-assisted monitoring of the human heart, enabling more reliable early diagnosis in wearable and clinical environments.
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.
Chronic diseases—such as diabetes, cardiovascular disorders, and chronic respiratory conditions—account for over 70% of global deaths annually, with late diagnosis being a primary contributor to poor outcomes. While machine learning (ML) models have shown promise in early detection, they often suffer from limited generalizability, data heterogeneity, and insufficient interpretability in clinical settings. This paper introduces a novel hybrid quantum-classical machine learning (HQML) framework that synergistically combines the pattern recognition power of classical deep neural networks with the high-dimensional optimization capabilities of quantum algorithms to enhance diagnostic accuracy, robustness, and early signal detection in chronic disease prediction. Using multimodal electronic health record (EHR) data—including clinical, genomic, and lifestyle variables—we train a quantum-enhanced feature selector followed by a classical interpretable classifier (e.g., SHAP-augmented XGBoost). The quantum component leverages a variational quantum circuit to identify non-linear, high-order feature interactions that classical methods often miss. Validated on datasets from the National Health and Nutrition Examination Survey (NHANES) and UK Biobank, our model achieves 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis—outperforming state-of-the-art baselines by 6.2%. Crucially, the framework maintains interpretability through post-hoc explainability and reduces data bias via fairness-aware quantum embedding. This research bridges quantum computing and public health, offering a scalable, ethically grounded diagnostic paradigm. By enabling earlier, more accurate, and equitable predictions, the HQML framework has significant potential to transform preventive care and reduce the global burden of chronic disease.
Abstract Classical deep learning models often struggle to fully capture complex visual patterns and to generalize effectively when trained on limited or imbalanced medical imaging data. To address this limitation, this paper presents a novel Hybrid Quantum Deep Learning (Hybrid QDL) framework that leverages the strengths of both deep neural networks and quantum circuits for advanced medical image interpretation. The proposed model integrates a pre-trained EfficientNet-B4 as the feature extractor and a variational quantum circuit as the classification head, enabling joint learning of hierarchical and quantum-enhanced representations. The Hybrid QDL model is evaluated on three clinically relevant datasets: COVIDx (for 3-class COVID-19 diagnosis using chest X-rays), PneumoniaMNIST (for pneumonia detection), and OrganAMNIST (for multi-organ abdominal classification). The model achieves state-of-the-art results, with 98.48% accuracy and 98.33% F1-score on COVIDx, outperforming both classical and prior quantum baselines. On PneumoniaMNIST and OrganAMNIST, it reaches 98.28% and 95.27% accuracy, respectively, along with high macro F1-scores. Ablation studies confirm that increasing quantum circuit depth and entanglement enhances discriminative power, particularly in complex multi-class scenarios. These results demonstrate that the Hybrid QDL framework is a robust, scalable solution for automated medical diagnosis and holds significant promise for real-world clinical decision support and COVID-19 detection.
Cardiovascular diseases (CVDs) continue to be the leading global cause of death, requiring innovative approaches for early detection and accurate prediction. This study explores the transformative potential of quantum-enhanced machine learning (queml) models in cardiovascular disease prediction, addressing the limitations of conventional diagnostic approaches. Using extensive datasets including the Kaggle Heart Disease Dataset of 1,190 patient records and the Cleveland/State Registry datasets, the quantum-enhanced models show superior performance over traditional machine learning approaches, with QCVNET achieving 91.73% accuracy and queml showing a 0.6% improvement in accuracy, while reducing training time from 862.5 µs to 670 µs per cycle. Key physiological parameters including diabetes indicators, LDL cholesterol, HDL cholesterol, and triglycerides were incorporated as predictive features. The IQCNN framework achieved 91% classification accuracy at a learning rate of 70%, significantly outperforming the Bi-LSTM and CNN approaches. Comparative analysis revealed exceptional AUC scores of 96.43% and 97.78% for the hybrid models, establishing quantum-enhanced machine learning as a highly effective solution for cardiovascular risk assessment and early detection systems.