Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning

The author argues that by harnessing inherent quantum noises, such as shot noise and incoherent noise, privacy can be preserved in Quantum Machine Learning (QML) models. They propose a method to achieve differential privacy (DP) in QML using these inherent noises.