A Hybrid Quantum–Classical Generative Adversarial Network for Synthetic Medical Data Generation and Disease Diagnosis

P.Mano Paul, Ruchitha K, Monish R, Shaswat Mishra, Mohamed Junaid, Aruna MG

Published Sep 1, 2026• 0 citations

Publisher

DOI: 10.1109/icosics70095.2026.11746647

Related papers

Hybrid Quantum-Classical Neural Networks for Medical Image Diagnosis

Mayank Genwa

International Conference on Cognitive Computing and Networking Systems (ICC-CNS)Jun 1, 20260 citations

QUGAN: Hybrid Generative Adversarial Networks with Variational Quantum Circuits for Synthetic Medical Data Generation

Ouail EL Bani, Chaïmaâ Kissi, Mohamed Nabil Srifi

The need for synthetic medical data is growing rapidly as machine learning becomes more important in healthcare, especially with strict privacy rules like HIPAA in the United States and GDPR in Europe. While Generative Adversarial Networks (GANs) have shown potential in generating realistic synthetic data, they often face problems such as mode collapse, unstable training, and difficulty handling complex, high-dimensional medical datasets.In this work, we introduce a new Quantum Generative Adversarial Network (QUGAN) that combines the strengths of quantum and classical computing. The generator is built using a Variational Quantum Circuit (VQC), while the discriminator remains a classical neural network. To our knowledge, this is the first time a QUGAN has been applied to structured tabular healthcare data and especially using a VQC circuit in the generator, which makes up most real electronic health records but is rarely explored in quantum machine learning. The model was developed step by step, starting from a simple GAN baseline, and gradually improved into a more powerful hybrid system.The QUGAN performed considerably better compared with the standard baseline when tested on actual cardiovascular dataset. While achieving synthetic precision scores of up to 92%, it decreased the Maximum Mean Discrepancy (MMD) by 50.6%, the Kullback–Leibler (KL) divergence by 65.6%, and the Jensen–Shannon (JS) divergence by 67.7%. These results indicate that the model creates diverse samples close to real data, which are a key factor for safe data sharing and dependable analysis later. In short, our findings suggest that QUGANs that uses VQC circuits can be reliable method to produce tabular medical datasets.

International Symposium on Advanced Electrical and Communication Technologies (ISAECT)Dec 18, 20250 citations

A Hybrid Classical-Quantum Machine Learning for the Binary Classification of Medical Images

Felipe Guicharrousse, Mauricio Solar

Lecture notes in networks and systemsJan 1, 20260 citations

Hybrid Quantum-Classical Generative Adversarial Network for Synthetic 12-Lead Electrocardiogram Signal Generation

Aron Jose A, A. Shamila Ebenezer

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.

International Conference on Recent Advancement in Electrical, Computer and Communication Technologies (IECCT)Apr 10, 20261 citation

Quantum Generative Adversarial Networks

Satyadhyan Chickerur, Vasavi Kumbargeri

Algorithms for intelligent systemsJan 1, 20225 citations

Synthetic data generation with hybrid quantum-classical models for the financial sector

Otto Menegasso Pires, Mauro Q. Nooblath Neto, Yan Alef Chagas Silva +3 more

Abstract Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets. Graphical abstract

The European Physical Journal BNov 1, 20246 citations