Theoretical Guarantees For Permutation-equivariant Quantum Neural Networks

This article discusses the use of Sn-equivariant quantum neural networks (QNNs) for supervised learning tasks. The authors provide guidelines for building these networks and prove their trainability and generalization capabilities. They also demonstrate these properties through numerical simulations. The article concludes by discussing the potential applications of Sn-equivariant QNNs in various scenarios.