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

Abstract

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.