Benefiting from cloud computing, today's early-stage quantum computers can be
remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS).
However, it poses a high risk of data leakage in quantum machine learning
(QML). To run a QML model with QaaS, users need to locally compile their
quantum circuits including the subcircuit of data encoding first and then send
the compiled circuit to the QaaS provider for execution. If the QaaS provider
is untrustworthy, the subcircuit to encode the raw data can be easily stolen.
Therefore, we propose a co-design framework for preserving the data security of
QML with the QaaS paradigm, namely PristiQ. By introducing an encryption
subcircuit with extra secure qubits associated with a user-defined security
key, the security of data can be greatly enhanced. And an automatic search
algorithm is proposed to optimize the model to maintain its performance on the
encrypted quantum data. Experimental results on simulation and the actual IBM
quantum computer both prove the ability of PristiQ to provide high security for
the quantum data while maintaining the model performance in QML.

提出了一个用于保护量子机器学习数据安全的共同设计框架 PrisitQ，通过引入一个带有用户定义安全密钥的附加安全量子位加密子电路，可以显著增强数据安全性，并提出了自动搜索算法来优化在加密量子数据上的模型以保持其性能。该框架在模拟和实际 IBM 量子计算机上的实验结果都证明了 PristiQ 提供高度安全性的能力，同时在量子机器学习中保持模型性能。