This study investigates hybrid quantum-classical K-Means algorithms for clustering heart disease patient data. Using quantum swap-test circuits for distance calculation, 2 approaches were tested on both noisy and ideal quantum simulators. A real-world dataset of over 1,000 records was preprocessed using standard techniques, including normalization, outlier removal, and dimensionality reduction. The results show that both quantum methods achieve accuracy up to 0.83 and F1-scores comparable to the classical K-Means baseline (0.82 - 0.83), even when executed on quantum simulators with real-device noise models. These findings highlight the practical potential of quantum-enhanced clustering methods. The study supports the feasibility of applying quantum-enhanced clustering in medical analytics, particularly for early-stage heart disease detection and patient risk stratification. HIGHLIGHTS This paper introduces a hybrid quantum–classical K-Means clustering framework for analyzing heart disease data. Two quantum circuits for distance calculation are designed and evaluated on a real-world dataset using both ideal quantum simulators and noisy quantum hardware (e.g., IBM’s ibm_brisbane). The quantum clustering algorithms demonstrate performance comparable to classical K-Means and traditional models such as XGBoost. Future work will focus on circuit optimization, scalable encoding strategies, and validation on larger datasets using real quantum devices. GRAPHICAL ABSTRACT
This study investigates hybrid quantum-classical K-Means algorithms for clustering heart disease patient data. Using quantum swap-test circuits for distance calculation, 2 approaches were tested on both noisy and ideal quantum simulators. A real-world dataset of over 1,000 records was preprocessed using standard techniques, including normalization, outlier removal, and dimensionality reduction. The results show that both quantum methods achieve accuracy up to 0.83 and F1-scores comparable to the classical K-Means baseline (0.82 - 0.83), even when executed on quantum simulators with real-device noise models. These findings highlight the practical potential of quantum-enhanced clustering methods. The study supports the feasibility of applying quantum-enhanced clustering in medical analytics, particularly for early-stage heart disease detection and patient risk stratification. HIGHLIGHTS This paper introduces a hybrid quantum–classical K-Means clustering framework for analyzing heart disease data. Two quantum circuits for distance calculation are designed and evaluated on a real-world dataset using both ideal quantum simulators and noisy quantum hardware (e.g., IBM’s ibm_brisbane). The quantum clustering algorithms demonstrate performance comparable to classical K-Means and traditional models such as XGBoost. Future work will focus on circuit optimization, scalable encoding strategies, and validation on larger datasets using real quantum devices. GRAPHICAL ABSTRACT
Abstract The development of Noisy Intermediate Scale Quantum computers is expected to signify potential advantages of quantum computing over classical computing. This paper focuses on quantum paradigm usage to speed up unsupervised machine learning algorithms particularly the K-means clustering method. The main approach is to build a quantum circuit that performs the distance calculation required for the clustering process. This proposed technique is a collaboration of data mining techniques with quantum computation. Initially extracted heart disease dataset is preprocessed and classical K-means performance is evaluated. Later, the quantum concept is applied to the classical approach of the clustering algorithm. The comparative analysis is performed between quantum and classical processing to check performance metrics.
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.
Cardiovascular diseases remain a leading global health challenge, driving the need for advanced, accurate diagnostic tools. While classical machine learning (ML) models like XGBoost have achieved significant success in medical diagnosis, they often face challenges related to accuracy plateaus and model explainability in complex, high-dimensional data spaces. Quantum machine learning (QML) emerges as a promising paradigm to potentially overcome these limitations by leveraging quantum computational advantages. This study aims to develop and evaluate a hybrid quantum-classical pipeline for the early detection of heart disease. We integrate robust data preprocessing—including missing value imputation, feature engineering, SMOTE for class balancing, and PCA for dimensionality reduction—with two classification approaches: a classical XGBoost model and a quantum Variational Quantum Classifier (VQC). Our results demonstrate that the XGBoost model achieves strong performance with an accuracy of 88.52% and an F1-score of 0.8852 on an imbalanced test set. While the VQC model did not surpass the classical baseline in the current noisy simulation environment, it shows theoretical promise. This work highlights the practical challenges of current QML and outlines a clear pathway for future research towards achieving a quantum advantage in clinical decision support systems.
To forecast heart problems, an innovative hybrid technique combining quantum computing with machine learning approaches is presented. For the preprocessing stage, normalization and dimensionality reduction techniques are used on a dataset of 270 patients' records and 13 clinical variables to make them appropriate for a 4-qubit quantum computer. Some traditional machine learning algorithms, including convolutional neural networks (CNN), support vector machines (SVM), and XGBoost, are trained to establish baselines in relation to other methods. Under the guidance of the SPSA algorithm, a quantum-inspired classifier called VQC is developed using a parameterized quantum circuit that includes rotation and entanglement operations. A quantitative study of crucial performance metrics reveals that the suggested quantum technique outperforms conventional methods in tests. The VQC classifier has the highest accuracy (85.2%), precision (86.4%), and F1 score (82.6%), while matching the SVM classifier's ROC-AUC score of 88.9%. Although with lower dimensionality, this research shows how quantum-based machine learning may find complex correlations among variables. This study is intended to serve as a preliminary concept for using quantum machine learning for healthcare applications, especially for increasing the accuracy rate and lowering misdiagnosis errors.
Cardiovascular diseases are currently the biggest cause of global death. A high portion of global mortality and expenditure on health are caused by such diseases, therefore, detecting abnormalities earlier and more correctly in cardiac conditions is essential for timely treatment. Diagnosis of the condition in communities or at clinical screening facilities uses techniques of electrocardiogram (ECG) and phonocardiogram (PCG). Nevertheless, actual patient assessment is hampered by the brief events, inter-patient variation, high levels of noise and erratic observation conditions. Today, deep learning techniques are improving automated screening of patients with potential cardiovascular conditions, but these methods achieve improved recognition results at the cost of larger model architecture, much greater processing load and significant susceptibility to variations in the quality and recording conditions of medical sensor recordings. Given these trade-offs, this research presents a set of quantum hybrid algorithms that use variational quantum circuits to carry out dense yet descriptive features for enhancing discriminability and stability in diagnosing cardiovascular disease. The research study focuses on three main challenges of detecting cardiovascular disease. First, arrhythmic events are often intermittent and subtle, making prompt ECG interpretation difficult, especially when input quality or resolution varies across devices and transmission conditions. Second, PCG recordings are vulnerable to noise and variability during recording, which can hinder generalisation in real-world screening settings. Third, ECG and PCG provide complementary electrical and mechanical insights into cardiac function. However, many automated methods analyse them separately or use traditional fusion strategies that do not fully leverage cross-modal interactions. To address these issues, the study presents three modular hybrid quantum algorithms that follow a consistent design philosophy and reusable components, facilitating the transition from unimodal classification to multimodal decision-making for cardiovascular disease detection. The first hybrid quantum algorithm for arrhythmia detection classifies multiple arrhythmia types by analysing their corresponding images. ECG readings are input to a pre-trained ResNet50 encoding model and are passed to the quantum classifier with a 10-qubit variational quantum circuit, simulated by PennyLane’s lightning.kokkos. Each image embedding then passes through a quantum convolutional layer before the final classification output is derived. Resolution of the input ECG signal is treated as an adjustable hyperparameter rather than a fixed input scale. The algorithm was tested on 123,998 images at 224x224 size drawn from the MIT-BIH Arrhythmia and PTB datasets. Performance was evaluated by setting test set resolutions between 80px and 320px. The algorithm consistently performed well at each resolution, with negligible calibration deviation and peak accuracy around 160px, offering practical trade-offs for its implementation. The algorithm reached a 99.98% prediction accuracy within 4.5 training hours. The integration of a variational quantum layer with a robust vision backbone shows that the algorithm is capable of performing direct ECG image classification and ensuring consistent performance across multiple resolutions, effectively rendering resolution as a flexible variable controllable by the end-user, not just a static fixed dimension. The second hybrid quantum algorithm classifies PCG signals for cardiovascular disease screening. PCG recordings derived from the PhysioNet/CinC 2016 dataset were transformed into Mel-spectrograms and fed into a ResNet50 feature extraction architecture linked with a 10-qubit variational quantum circuit executed using lightning.kokkos. In addition, Gaussian noise was added to verify the robustness of the presented algorithm against different levels of acoustic clutter typically encountered in a real clinical setting. This methodology resulted in significant outcomes, achieving 99.34% accuracy, 99.33% sensitivity and 99.35% specificity for clear PCG signals. The achieved performance was comparable with, or higher than, several previously reported PCG classification studies, although direct comparison is limited by differences in datasets and evaluation protocols.. In the presence of noisy PCG data, the algorithm also exhibited robust performance, reaching 96.68% accuracy at 20 dB SNR and 80.73% accuracy at 10 dB SNR, indicating tolerance to mild and moderate synthetic noise. This hybrid quantum machine learning approach innovates by combining a variational quantum circuit with spectrogram analysis and evaluating the tolerance of the algorithm to noise, suggesting its potential to maintain high accuracy despite realistic environmental interference. The third algorithm uses a Hybrid Quantum Fusion Network (HQFN) to combine ECG and PCG classifier-output representations through a variational quantum fusion layer. The ECG inputs were six-dimensional output-logit vectors derived from the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets, while the PCG inputs were two-dimensional outputlogit vectors derived from the PhysioNet/CinC 2016 dataset. Because the ECG and PCG data were obtained independently and were neither synchronised nor patient-matched, the experiment was framed as proof-of-concept class-consistent output-level fusion rather than patient-level multimodal diagnosis. Normal ECG outputs were paired with Normal PCG outputs, while Abnormal ECG outputs were paired with Abnormal PCG outputs. The source representations were split before pairing, and SMOTE was applied only to the training representation space. The combined eight-dimensional input was processed by an eight-qubit variational quantum circuit and mapped to a binary Normal/Abnormal decision through a classical output layer. On the held-out test set, the quantum fusion algorithm achieved 99.50% accuracy, with macro-averaged precision, recall and F1-score also at 99.50%, specificity of 99.01% and ROC–AUC of 0.999994. However, a compact classical fusion baseline achieved 100% accuracy under the same experimental conditions. Therefore, the results do not demonstrate a quantum performance advantage. Instead, they establish the feasibility of applying a variational quantum circuit to compact ECG and PCG classifier outputs within a controlled output-level fusion framework. Future work should evaluate patient-matched and synchronised recordings, richer intermediate representations, discordant modality pairs and parameter-matched classical baselines. This study presents three scalable modular hybrid quantum algorithms tailored for three methods: the use of ECG only, the use of PCG only and a hybrid method that combines both ECG and PCG signals to improve the accuracy of the detection of cardiovascular diseases. Results show that quantum machine learning could potentially generate highly accurate values about the classification of both arrhythmias and heart sounds, even with issues that commonly exist in real-world scenarios, such as lower resolution inputs and added noise. Moreover, the presented algorithms are intended to function as versatile reusable blocks capable of expanding from single modality detection up to the fusion of different cardiovascular signals through quantum computation, paving the way for the utilisation of quantum machine learning in future cardiovascular disease screening.
Heart diseases are a set of heart and blood-vessel-related disorders, arising from a variety of lifestyle, environmental, and genetic factors. Even with advancements in medical technology, accurately forecasting heart attack risk remains challenging, as today’s diagnosis methods fail to catch the nonlinearity and complexity in clinical data. Classical machine learning algorithms, such as k-nearest neighbors and support vector machine, when mixed with synthetic minority oversampling technique, show promise for heart attack risk prediction. These models for real-world scenarios struggle with high-dimensional, imbalanced, nonlinear datasets, which limits the effectiveness of models. Quantum machine learning offers a computational approach and takes benefit of concepts like superposition and entanglement to uncover insightful patterns. In this work, we compare classical and quantum models for heart attack risk prediction using a heart attack prediction dataset with 14 clinical features and 1888 patient records. The outcomes demonstrate that the classical support vector machine, when combined with synthetic minority oversampling technique, achieves the highest predictive accuracy of 92.00%, with minimal run time. k-nearest neighbors also works well, with an accuracy of 83.33%. Among quantum methods, quantum support vector machine with quantum Fourier transform achieves a 74.00% accuracy, while quantum clustering reaches 67.20%. However, both approaches required higher computational power. The quantum Fourier transform–based approach improves quantum model performance, particularly in recognizing cyclic trends in clinical data. However, classical approaches remain superior with respect to accuracy and efficiency on current hardware. This work demonstrates the strength of quantum machine learning in handling complex data while also recognizing the present challenges with quantum hardware and simulators. It suggests that upcoming research should focus on testing these models on actual quantum devices. Adopting standardized performance metrics can improve both classical and quantum-based decision support systems for heart disease risk prediction.