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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 $R^2{=}0.829$ vs.\\ MLP$_{720}$'s $0.753$ on Boston Housing, 3-seed average); \\textbf{(ii)}~FC-VQC's Type~4 inter-block connectivity provides partial cross-token mixing that approximates the role of attention -- explicit quantum self-attention yields only m","title":"Do Quantum Transformers Help? 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