Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits
FC-VQC introduces a modular framework for quantum machine learning by breaking down large inputs into smaller, local quantum circuits connected by fixed block-mixing rules. This allows trainable parameters to scale with input size, overcoming limits of monolithic circuits. It achieves competitive performance against deep neural networks while using significantly fewer trainable parameters.