Rethinking Expressibility-Trainability Trade-off in Hybrid Quantum Neural Networks
This paper challenges the assumed expressibility-trainability trade-off in hybrid quantum neural networks by demonstrating that classical components reshape the optimization landscape to decouple these metrics, thereby necessitating a multi-objective neural architecture search framework to optimize hybrid designs.