The paper introduces TabularQGAN, a quantum generative adversarial network designed to synthesize tabular data with both categorical and numerical features. It proposes flexible data encoding and a novel quantum circuit ansatz, and evaluates the model on MIMIC‑III and Adult Census datasets, benchmarking against classical methods such as CTGAN, CopulaGAN, VAE‑GMM, and an LLM‑based approach. Results from noiseless statevector simulations show competitive or leading performance in overall similarity scores and demonstrate strong generalization through custom metrics.
By Pallavi Bhardwaj, Caitlin Jones, Lasse Dierich, Aleksandar Vu\v{c}kovi\'c
arXiv:2608.31117v1 Announce Type: cross
Abstract: Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum ge...
By Snehal Raj, Natansh Mathur, Alejandro Perdomo-Ortiz
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
By Hao-Yuan Chen
arXiv:2607. 13466v1 Announce Type: new Abstract: Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored.
By Mingzhu Wang, Yun Shang
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
By Chi-Sheng Chen, En-Jui Kuo
Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.