Based on the linearity of quantum unitary operations, we propose a method that runs the parameterized quantum circuits before encoding the input data. This enables a dataset owner to train machine learning models on quantum cloud computation platforms, without the risk of leaking the information about the data. It is also capable of encoding a vast amount of data effectively at a later time using classical computations, thus saving runtime on quantum computation devices. The trained quantum machine learning models can be run completely on classical computers, meaning the dataset owner does not need to have any quantum hardware, nor even quantum simulators. Moreover, our method mitigates the encoding bottleneck by reducing the required circuit depth from $O(2^{n})$ to $O(n)$, and relax the tolerance on the precision of the quantum gates for the encoding. These results demonstrate yet another advantage of quantum and quantum-inspired machine learning models over existing classical neural networks, and broaden the approaches to data security.

翻译：基于量子幺正操作的线性特性，我们提出一种在输入数据编码前运行参数化量子线路的方法。该方法使得数据集所有者能够在量子云计算平台上训练机器学习模型，同时避免数据信息泄露的风险。该方法还能在后期通过经典计算高效编码海量数据，从而节省量子计算设备的运行时间。训练完成的量子机器学习模型可完全在经典计算机上运行，这意味着数据集所有者无需拥有任何量子硬件，甚至不需要量子模拟器。此外，我们的方法将所需线路深度从$O(2^{n})$降低至$O(n)$，从而缓解了编码瓶颈，同时降低了对编码量子门精度的容错要求。这些结果进一步证明了量子及量子启发的机器学习模型相较于现有经典神经网络的优势，并拓宽了数据安全的实现途径。