Due to the stringent hardware requirements and high cost, quantum computing as a service (QCaaS) is currently the main way to output quantum computing capabilities. However, the current QCaaS has significant shortcomings in privacy protection. The existing researches mainly focus on dataset privacy in some specific quantum machine learning algorithms, and there is no general and comprehensive research on privacy protection for dataset, parameter sets and algorithm models. To solve this problem, this paper defines the concept of generalized quantum homomorphic encryption and pioneers a novel method termed quantum circuit equivalence homomorphic encryption (QCEHE), aiming at protecting the privacy of the complete quantum circuits—encompassing data, parameters, and model. Based on QCEHE, a privacy protection scheme and its approximate implementation called quantum circuit equivalent substitution algorithm are proposed for any quantum algorithm, which can encrypt the complete quantum circuit on a classical computer, ensuring that the encrypted quantum circuit is physically equivalent to the original one, and does not reveal data holders’ privacy (data, parameters and model). By theoretical derivation, we prove that the proposed solution can effectively execute any quantum algorithm while protecting privacy. By applying the proposed solution to the privacy protection of the Harrow–Hassidim–Lloyd algorithm and the variational quantum classifier algorithm, the results showed that the accuracy rate before and after encryption are almost the same, which means that the proposed solution can effectively protect the privacy of data holders without impacting the usability and accuracy.

中文翻译：

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量子计算中普遍且全面的隐私保护：一种通过量子电路等价同态加密的新方法


由于硬件要求严格、成本较高，量子计算即服务（QCaaS）是目前量子计算能力输出的主要方式。然而，目前的QCaaS在隐私保护方面存在明显缺陷。现有的研究主要集中在一些特定量子机器学习算法中的数据集隐私，而没有对数据集、参数集和算法模型的隐私保护进行普遍、全面的研究。为了解决这个问题，本文定义了广义量子同态加密的概念，并首创了一种称为量子电路等效同态加密（QCEHE）的新方法，旨在保护完整量子电路（包括数据、参数和模型）的隐私。基于QCEHE，针对任何量子算法，提出了一种隐私保护方案及其近似实现，称为量子电路等效替换算法，可以在经典计算机上加密完整的量子电路，保证加密后的量子电路与原始量子电路在物理上等效，并且不泄露数据持有者的隐私（数据、参数和模型）。通过理论推导，我们证明所提出的解决方案可以有效执行任何量子算法，同时保护隐私。将所提出的解决方案应用于Harrow-Hassidim-Lloyd算法和变分量子分类器算法的隐私保护，结果表明加密前后的准确率几乎相同，这意味着所提出的解决方案可以有效保护隐私。保护数据持有者的隐私，而不影响可用性和准确性。