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.

翻译：量子计算彻底改变了解决复杂问题和处理海量数据的方式，展现出加速机器学习过程的巨大潜力。然而，量子机器学习（QML）中的数据泄露可能带来隐私风险。尽管差分隐私（DP）通过注入人工噪声来保护隐私是一种成熟方法，但其在QML领域中的应用仍鲜有探索。本文提出利用量子固有噪声来保护QML中的数据隐私。特别地，针对含噪中等规模量子（NISQ）设备，我们利用量子计算中不可避免的散粒噪声和非相干噪声来保护用于二分类的QML模型的隐私。我们通过数学分析证明QML中量子电路参数的梯度服从高斯分布，并推导了其方差的上下界，这有望提供DP保证。仿真结果表明，通过运行不同次数的量子电路可以实现目标隐私保护水平。