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：量子计算革新了解决复杂问题与处理大规模数据集的方式，在加速机器学习过程中展现出巨大潜力。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）通过注入人工噪声保护隐私是一种成熟方法，但其在QML领域的应用仍有待探索。本文提出利用量子固有噪声保护QML中的数据隐私。具体而言，针对含噪声中等规模量子（NISQ）器件，我们利用量子计算中不可避免的散粒噪声和非相干噪声，保护二分类QML模型的隐私。通过数学分析，我们证明了QML中量子电路参数梯度服从高斯分布，并推导出其方差的上下界，这能为DP保障提供潜在可能。仿真结果表明，通过改变量子电路的运行次数，可实现目标隐私保护水平。