Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks

arXiv:2210.09974v3 Announce Type: replace-cross
Abstract: Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer from excessive local minima and barren plateaus in their training landscapes. Recently, the nascent field of geometric quantum machine learning (GQML) has emerged as a potential solution to some of those issues. The key insight of GQML is that one should design architectures, …

abstract arxiv challenges cs.lg instance machine machine learning machine learning models networks neural networks quant-ph quantum quantum neural networks stat.ml training type