Negational symmetry of quantum neural networks for binary pattern classification

作者：

Highlights：

• Formalize, prove, and analyze the negational symmetry of QNNs with full entanglement in binary pattern classification.

• Propose a representation learning framework for QNNs and generalize the negational symmetry to it.

• Show that the negational symmetry of QNNs could be a double-edged sword in potential applications.

摘要

•Formalize, prove, and analyze the negational symmetry of QNNs with full entanglement in binary pattern classification.•Propose a representation learning framework for QNNs and generalize the negational symmetry to it.•Show that the negational symmetry of QNNs could be a double-edged sword in potential applications.

论文关键词：Deep learning,Quantum machine learning,Binary pattern classification,Representation learning,Symmetry

论文评审过程：Received 10 August 2021, Revised 19 April 2022, Accepted 25 April 2022, Available online 27 April 2022, Version of Record 2 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108750