Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
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. 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 ac
The increased maturity of quantum computing hardware and algorithms, coupled with growing research into quantum machine learning, makes this a timely evaluation of VQC architectures.
This research provides critical guidance on the efficacy of different quantum circuit architectures for machine learning tasks, influencing the future direction of quantum AI development.
Our understanding of which quantum circuit designs are most effective for specific machine learning problems, particularly regarding the performance trade-offs of quantum transformers.
- · Quantum Machine Learning Researchers
- · Quantum Software Developers
- · Companies investing in Quantum AI
- · Developers using suboptimal VQC architectures
- · Classical machine learning approaches in specialized niches
Refined quantum machine learning algorithms will emerge based on these architectural insights.
Improved quantum AI performance could accelerate specific applications in fields like materials science or drug discovery.
The development pathway for general-purpose quantum computers might be influenced by the demands of optimized VQC architectures.
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