Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
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. We present a systematic empirical comparison of four VQC families -- multi-layer fully-connected (FC-VQC), residual (ResNet-VQC), hybrid quantum-classical transformer (QT), and fully quantum transformer (FQT) -- across five regression and classification benchmarks. Our key findings are: \textbf{(i)}~FC-VQCs achieve 90-96\% of the $R^2$ of attention-based VQCs while using 40-50\% fewer parameters, and consistently outperform equal-capacity MLPs (mean $R^2{=}0.829...
| Authors | Chi-Sheng Chen, En-Jui Kuo |
|---|---|
| Primary category | quant-ph |
| Published | 27 April 2026 |
| Last updated at source | 7 July 2026 |
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