A quantum GAN with one-hot-preserving Givens rotations generates synthetic tabular data that matches real data better (SDMetrics similarity) than CTGAN and CopulaGAN on three- to four-feature subsets of two public datasets, in noiseless simulation.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
TabularQGAN: A Quantum Generative Model for Tabular Data
A quantum GAN with one-hot-preserving Givens rotations generates synthetic tabular data that matches real data better (SDMetrics similarity) than CTGAN and CopulaGAN on three- to four-feature subsets of two public datasets, in noiseless simulation.