Improving Trainability of Variational Quantum Circuits via Regularization Strategies

arXiv:2405.01606v1 Announce Type: cross
Abstract: In the era of noisy intermediate-scale quantum (NISQ), variational quantum circuits (VQCs) have been widely applied in various domains, advancing the superiority of quantum circuits against classic models. Similar to classic models, regular VQCs can be optimized by various gradient-based methods. However, the optimization may be initially trapped in barren plateaus or eventually entangled in saddle points during training. These gradient issues can significantly undermine the trainability of VQC. In this work, we propose a …

abstract arxiv circuits cs.lg domains gradient however improving intermediate nisq optimization quant-ph quantum regularization scale strategies type via