  Quantum computing revolutionizes the way of solving complex problems and
handling vast datasets, which shows great potential to accelerate the machine
learning process. However, data leakage in quantum machine learning (QML) may
present privacy risks. Although differential privacy (DP), which protects
privacy through the injection of artificial noise, is a well-established
approach, its application in the QML domain remains under-explored. In this
paper, we propose to harness inherent quantum noises to protect data privacy in
QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ)
devices, we leverage the unavoidable shot noise and incoherent noise in quantum
computing to preserve the privacy of QML models for binary classification. We
mathematically analyze that the gradient of quantum circuit parameters in QML
satisfies a Gaussian distribution, and derive the upper and lower bounds on its
variance, which can potentially provide the DP guarantee. Through simulations,
we show that a target privacy protection level can be achieved by running the
quantum circuit a different number of times.
