FC-VQC decomposes inputs into fixed-size local VQC blocks connected by deterministic mixing to achieve linear parameter scaling and competitive performance versus monolithic VQCs and DNNs on regression, classification, and PDE tasks.
Experimental Setup This appendix provides detailed specifications for the datasets, baseline models, and training protocols used in our experiments
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Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits
FC-VQC decomposes inputs into fixed-size local VQC blocks connected by deterministic mixing to achieve linear parameter scaling and competitive performance versus monolithic VQCs and DNNs on regression, classification, and PDE tasks.