Quantum machine learning with differential privacy

Understand

Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing.

• A quantum advantage arises due to the intractability of quantum operations on a classical computer.
• Many datasets used in machine learning are crowd sourced or contain some private information.
• To the best of our knowledge, no current QML models are equipped with privacy-preserving features, which raises concerns as it is paramount that models do not expose sensitive information.