TabularQGAN: A Quantum Generative Model for Tabular Data

TL;DR

This paper introduces TabularQGAN, a quantum generative adversarial network designed to synthesize tabular data, outperforming classical models in accuracy and efficiency, and demonstrating the potential of quantum computing for data generation tasks.

Contribution

The paper presents a novel quantum GAN architecture with a unique quantum circuit ansatz tailored for tabular data, achieving superior performance over classical models.

Findings

Quantum model outperforms classical models by 8.5% in similarity score.

Uses only 0.072% of classical model parameters.

Successfully generates useful and novel tabular data samples.

Abstract

In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or private, it can be used to augment or replace existing datasets. Real-world enterprise data is predominantly tabular and heterogeneous, often comprising a mixture of categorical and numerical features, making it highly relevant across various industries such as healthcare, finance, and software. We propose a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz to effectively model tabular data. The proposed approach is tested on the MIMIC III healthcare and Adult Census datasets, with extensive benchmarking against leading classical models, CTGAN, and CopulaGAN. Experimental results demonstrate that our quantum model outperforms classical models by an average of 8.5% with respect to an overall similarity score from SDMetrics, while using only 0.072% of the parameters of the classical models. Additionally, we evaluate the generalization capabilities of the models using two custom-designed metrics that demonstrate the ability of the proposed quantum model to generate useful and novel samples. To our knowledge, this is one of the first demonstrations of a successful quantum generative model for handling tabular data, indicating that this task could be well-suited to quantum computers.