Theoretical guarantees for permutation-equivariant quantum neural networks
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 Espresso Cups nascent field of