Abstract
Variational quantum algorithms (VQAs) are considered as one of the most promising candidates for achieving quantum advantages on quantum devices in the noisy intermediate-scale quantum (NISQ) era. They have been developed for numerous applications such as image processing and solving linear systems of equations. The application of VQAs can be greatly enlarged if users with limited quantum capabilities can run them on remote powerful quantum computers. But the private data of clients may be leaked to quantum servers in such a quantum cloud model. To solve the problem, a novel quantum homomorphic encryption (QHE) scheme which is client-friendly and suitable for VQAs is constructed for quantum servers to calculate encrypted data. Then delegated VQAs are proposed based on the given QHE scheme, where the server can train the ansatz circuit using the client's data even without knowing the real input and the output of the client. Furthermore, a delegated variational quantum classifier to identify handwritten digit images is given as a specific example of delegated VQAs and simulated on the cloud platform of Original Quantum to show its feasibility.
Abstract (translated)
变分量子算法(VQAs)被认为是在噪声中等规模量子(NISQ)时代的量子设备上实现量子优势的最有前途候选人之一。它们被开发用于许多应用,例如图像处理和解决线性方程组。如果用户具有有限的量子能力可以在远程强大的量子计算机上运行它们,那么VQAs的应用可以极大地扩大。但是,客户的 private 数据可能在这种量子云模型中泄露到量子服务器上。为了解决这个问题,为量子服务器构建了一个友好的VQAs适合客户的新量子同态加密(QHE)方案,该方案适合VQAs,使得服务器可以使用客户的数据训练同义模型电路,即使不知道客户的真正输入和输出。此外,给出一个委托给VQAs的变分量子分类器以识别手写数字图像的具体例子,并模拟在原始量子云平台上展示其可行性。
URL
https://arxiv.org/abs/2301.10433
