ECG-QGAN: A ECG Generative Information System Based on Quantum Generative Adversarial Networks

Qu Zhiguo; Chen Weilong; Sun Le; Liu Wenjie; Zhang Yanchun

doi:10.7544/issn1000-1239.202440527

Journal of Computer Research and Development > 2025 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440527 CSTR: 32373.14.issn1000-1239.202440527

Qu Zhiguo, Chen Weilong, Sun Le, Liu Wenjie, Zhang Yanchun. ECG-QGAN: A ECG Generative Information System Based on Quantum Generative Adversarial Networks[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440527

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ECG-QGAN: A ECG Generative Information System Based on Quantum Generative Adversarial Networks

1.
School of Computer Science & School of Cyber Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044
2.
School of Software, Nanjing University of Information Science and Technology, Nanjing 210044
3.
School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004
4.
Department of New Networks, Peng Cheng Laboratory, Shenzhen, Guangdong 518066

Funds: This work was supported by the Science and Technology Innovation 2030- Major Project （2021ZD0302901）, the National Natural Science Foundation of China (61373131, 62071240), the Open Foundation of State Key Laboratory of Networking and Switching Technology （SKLNST-2020-1-17), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology.

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Author Bio:
Qu Zhiguo: born in 1976. PhD, associate professor. Member of CCF. His main research interests include quantum machine learning, deep learning, blockchain and quantum blockchain, quantum secure communication

Chen Weilong: born in 2000. Master. His main research interests include quantum neural networks

Sun Le: born in 1987. PhD, professor. Member of CCF. Her main research interests include graph neural networks, energy-efficient smart healthcare, data mining

Liu Wenjie: born in 1979. PhD, associate professor. Member of CCF. His main research interests include quantum secure multi-party computation; quantum cryptanalysis; quantum machine learning; knowledge graph and graph neural network; large model

Zhang Yanchun: born in 1958. PhD, professor. His main research interests include database, data mining, health informatics, web information systems, and web services
Received Date: May 31, 2024
Revised Date: February 27, 2025
Accepted Date: April 02, 2025
Available Online: April 02, 2025

Graphical Abstract

Abstract

Abstract

According to statistics, the number of people suffering from cardiovascular diseases in China is about 330 million, and the number of deaths caused by cardiovascular diseases accounts for 40% of the total number of deaths each year. Under this circumstance, the development of heart disease assisted diagnosis systems is particularly important, but its development is limited by the lack of a large amount of electrocardiogram (ECG) clinical data that does not contain patient privacy information and needs to be annotated by medical experts. As an emerging discipline, quantum computing can explore larger and more complex state spaces by utilizing quantum superposition and entanglement properties, which is beneficial for generating high-quality and diverse electrocardiogram data similar to real clinical data. Therefore, we propose an electrocardiogram generative information system based on quantum generative adversarial networks, abbreviated as ECG-QGAN. The quantum generative adversarial network consists of a quantum bidirectional gated recurrent unit (QBiGRU) and a quantum convolutional neural network (QCNN). The system utilizes the entanglement property of quantum to improve the generative capability to produce ECG data that is consistent with the existing clinical data so that the heartbeat characteristics of cardiac patients can be preserved. The generator and discriminator of this system use QBiGRU and QCNN, respectively. The variational quantum circuit (VQC) designed based on matrix product state (MPS) and tree tensor network (TTN) is adopted, which enables the system to capture ECG data information more efficiently and generate qualified ECG data with fewer quantum resources. In addition, the system applies quantum Dropout technology to avoid overfitting issues during the training process. Finally, the experimental results show that the ECGs generated by ECG-QGAN have a higher average classification accuracy compared to other models for generating ECGs. It is also friendly to the current noisy intermediate scale quantum (NISQ) computers in terms of the number of quantum bits and circuit depth.

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References (36)

References

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