QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis

TL;DR

QTabGAN introduces a hybrid quantum-classical GAN framework that leverages quantum circuits to improve the synthesis of realistic, high-dimensional tabular data, especially under data scarcity or privacy constraints.

Contribution

The paper presents a novel hybrid quantum-classical GAN architecture specifically designed for tabular data synthesis, demonstrating improved performance over existing models.

Findings

Achieves up to 54.07% improvement on classification datasets

Effectively models complex data distributions with quantum circuits

Establishes potential for scalable quantum-assisted data generation

Abstract

Synthesizing realistic tabular data is challenging due to heterogeneous feature types and high dimensionality. We introduce QTabGAN, a hybrid quantum-classical generative adversarial framework for tabular data synthesis. QTabGAN is especially designed for settings where real data are scarce or restricted by privacy constraints. The model exploits the expressive power of quantum circuits to learn complex data distributions, which are then mapped to tabular features using classical neural networks. We evaluate QTabGAN on multiple classification and regression datasets and benchmark it against leading state-of-the-art generative models. Experiments show that QTabGAN achieves up to 54.07% improvement across various classification datasets and evaluation metrics, thus establishing a scalable quantum approach to tabular data synthesis and highlighting its potential for quantum-assisted generative modelling.