Although cardiovascular disease constitutes the primary cause of death in China, the clinical deployment of artificial intelligence-assisted diagnostic tools remains constrained by the limited availability of large-scale, high-fidelity electrocardiogram datasets with adequate privacy safeguards. Conventional generative adversarial networks (GANs) encounter persistent constraints regarding temporal sequence representation, elevated-dimensional attribute extraction, and optimization stability, concurrently failing to accommodate resource limitations inherent to contemporary noisy intermediate-scale quantum (NISQ) apparatus. Drawing inspiration from ensemble methodologies typified by random forests, this investigation introduces an ECG generative information architecture designated QRFGAN-ECG, predicated upon quantum random forest generative adversarial networks. The generative component implements quantum residual recurrent networks (QResRNN) to encapsulate extended-range cardiac cycle dependencies; conversely, the discriminative module incorporates quantum random forest convolutional networks (QRF-CNN) to execute multi-resolution attribute voting, thereby augmenting discrimination robustness. Variational quantum circuit architectures formulated through matrix product states (MPS) and tree tensor networks (TTN) substantially diminish qubit requirements and circuit depth. Furthermore, this study pioneerly integrates quantum dropout mechanisms with forest ensemble strategies to mitigate quantum overfitting phenomena. Empirical evaluations conducted across four publicly accessible ECG repositories demonstrate QRFGAN-ECG superiority over classical Against BiGRU-CNN, ECG-QGAN, and DCGAN baselines, the proposed framework demonstrates superior performance across Pearson correlation coefficient, percentage root-mean-square difference, root-mean-square error, and downstream classification accuracy. Adversarial training convergence improves by roughly one-third, with markedly enhanced robustness to four dominant quantum noise regimes. The generated ECG samples carry intrinsic labels, eliminating manual annotation overhead while precluding privacy exposure. These synthetic specimens readily support training set expansion, establishing a dependable data foundation for cardiac disease diagnostic assistance systems.
Although cardiovascular disease constitutes the primary cause of death in China, the clinical deployment of artificial intelligence-assisted diagnostic tools remains constrained by the limited availability of large-scale, high-fidelity electrocardiogram datasets with adequate privacy safeguards. Conventional generative adversarial networks (GANs) encounter persistent constraints regarding temporal sequence representation, elevated-dimensional attribute extraction, and optimization stability, concurrently failing to accommodate resource limitations inherent to contemporary noisy intermediate-scale quantum (NISQ) apparatus. Drawing inspiration from ensemble methodologies typified by random forests, this investigation introduces an ECG generative information architecture designated QRFGAN-ECG, predicated upon quantum random forest generative adversarial networks. The generative component implements quantum residual recurrent networks (QResRNN) to encapsulate extended-range cardiac cycle dependencies; conversely, the discriminative module incorporates quantum random forest convolutional networks (QRF-CNN) to execute multi-resolution attribute voting, thereby augmenting discrimination robustness. Variational quantum circuit architectures formulated through matrix product states (MPS) and tree tensor networks (TTN) substantially diminish qubit requirements and circuit depth. Furthermore, this study pioneerly integrates quantum dropout mechanisms with forest ensemble strategies to mitigate quantum overfitting phenomena. Empirical evaluations conducted across four publicly accessible ECG repositories demonstrate QRFGAN-ECG superiority over classical Against BiGRU-CNN, ECG-QGAN, and DCGAN baselines, the proposed framework demonstrates superior performance across Pearson correlation coefficient, percentage root-mean-square difference, root-mean-square error, and downstream classification accuracy. Adversarial training convergence improves by roughly one-third, with markedly enhanced robustness to four dominant quantum noise regimes. The generated ECG samples carry intrinsic labels, eliminating manual annotation overhead while precluding privacy exposure. These synthetic specimens readily support training set expansion, establishing a dependable data foundation for cardiac disease diagnostic assistance systems.
The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.
The development of effective deep learning systems for cardiac disease diagnosis requires extensive and diverse electrocardiogram (ECG) databases, which remain scarce due to patient confidentiality regulations and costly medical annotation procedures. This paper introduces a novel hybrid Quantum Generative Adversarial Network (QGAN) that generates realistic 12-lead ECG signals trained on the MIT-BIH Arrhythmia Database. A parameterised quantum circuit (PQC) serves as the generative core, exploiting quantum superposition and entanglement to model biomedical signal distributions that traditional generators cannot efficiently replicate. The system pairs a PennyLane-based quantum generator with a 1D Convolutional Neural Network discriminator implemented in PyTorch, trained via the parameter-shift rule. Experimental results demonstrate that the Quantum PQC achieves a Fréchet Distance of 12.3 ±2.1, outperforming Classical CNN (15.8 ±3.2) and Classical LSTM (18.4 ±4.1) baselines while using fewer parameters. Generated 12-lead traces accurately reproduce P-waves, QRS complexes, T-waves, and inter-lead spatial relationships, validating quantum-augmented synthesis as a viable, privacy-preserving approach for cardiovascular AI training.
To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG.
Quantum Generative Adversarial Networks (QGANs) represent a useful development in quantum machine learning, using the particular properties of quantum mechanics to solve the challenges of data analysis and modeling. This paper brings up a general analysis of five QGAN architectures, focusing on their evolution, strengths, weaknesses, and limitations in noisy intermediate-scale quantum (NISQ) devices. Primary methods like Entangling Quantum GAN (EQ-GAN) and Quantum state fidelity (QuGAN) concentrate on stability, convergence, and robust performance on small-scale datasets such as 2 × 2 grayscale images. Intermediate models such as Image Quantum GAN (IQGAN) and Experimental Quantum GAN (EXQGAN) provide new ideas like trainable encoders and patch-based sub-generators that are scalable to 8 × 8 datasets with increasing noise resilience. The most advanced method is Parameterized Quantum Wasserstein GAN (PQWGAN), which uses a hybrid quantum-classical structure to obtain high-resolution image processing for 28 × 28 grayscale datasets while trying to maintain parameter efficiency. This study explores, analyzes, and summarizes critical problems of QGANs, including accuracy, convergence, parameter efficiency, image quality, performance metrics, and training stability under noisy conditions. In addition, developing QGANs can generate and train parameters in quantum approximation optimization algorithms. One of the useful applications of QGAN is generating medical datasets that can generate medical images from limited datasets to train specific medical models for the recognition of diseases.
Synthetic biological data play a crucial role in bioinformatics research, particularly in scenarios involving limited data availability and strict privacy constraints. However, conventional generative adversarial networks often face challenges in simultaneously preserving local statistical characteristics and global data distributions, motivating the exploration of quantum-enhanced generative models. This paper proposes and evaluates a hybrid quantum–classical generative adversarial network (QGAN) for efficient and high-quality biological data generation. The generator is implemented using a variational quantum circuit that encodes classical biological features into parameterized quantum states, while the discriminator is realized through a classical neural network architecture. The proposed framework is evaluated against classical GAN models using quantitative metrics, including the Classifier Two-Sample Test (C2ST), Maximum Mean Discrepancy (MMD) and Wasserstein Distance (WD). Experimental results show that the QGAN achieves stronger local statistical distribution matching, reflected by lower MMD values, whereas the Classical GAN demonstrates more stable global distribution alignment through lower WD. Furthermore, the analysis reveals that the stochastic sampling behavior of parameterized quantum circuits contributes to higher output variance during training, influencing large-scale distribution consistency. These findings indicate that hybrid quantum–classical generative models can effectively preserve localized statistical structures while highlighting the trade-off between local fidelity and global distribution stability, supporting their potential for future applications in genomics, drug discovery, and privacy-preserving biomedical data synthesis.
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.