  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. Thus, privacy-preserving algorithms need to be
implemented with QML. One solution is to make the machine learning algorithm
differentially private, meaning the effect of a single data point on the
training dataset is minimized. Differentially private machine learning models
have been investigated, but differential privacy has yet to be studied in the
context of QML. In this study, we develop a hybrid quantum-classical model that
is trained to preserve privacy using differentially private optimization
algorithm. This marks the first proof-of-principle demonstration of
privacy-preserving QML. The experiments demonstrate that differentially private
QML can protect user-sensitive information without diminishing model accuracy.
Although the quantum model is simulated and tested on a classical computer, it
demonstrates potential to be efficiently implemented on near-term quantum
devices (noisy intermediate-scale quantum [NISQ]). The approach's success is
illustrated via the classification of spatially classed two-dimensional
datasets and a binary MNIST classification. This implementation of
privacy-preserving QML will ensure confidentiality and accurate learning on
NISQ technology.
