AI RESEARCH
Resource-efficient equivariant quantum convolutional neural networks
arXiv CS.LG
•
ArXi:2410.01252v2 Announce Type: replace-cross Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities. Despite theoretical developments in equivariant QNNs, their implementation on near-term quantum devices remains challenging due to limited computational resources. This study proposes a resource-efficient model of equivariant quantum convolutional neural networks (QCNNs) called equivariant split-parallelizing QCNN (sp.