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.
翻译:变量算法(VQAs)被认为是在吵闹的中间尺度量子(NISQ)时代在量子装置上实现量子优势的最有希望的候选者之一,这些算法是为图像处理和解决直线等式系统等许多应用而开发的。如果量子能力有限的用户能够用远程强大的量子计算机运行这些算法,VQAs的应用可以大为扩大。但是客户的私人数据可能会以这种量子云模式泄露给量子服务器。为了解决问题,为量子服务器创建了一个对客户友好和适合VQAs的量子同质加密(QHE)方案,用于计算加密数据。然后根据给定的QHE计划提议授予VQAs,服务器可以在不了解客户实际输入量子计算机和输出的情况下,利用客户的数据来培训asatz电路。此外,一个授权的量子分析器来识别手写数字图像的量子分析器,作为授权VQAs的具体例子,并模拟了原Quantum的云台显示其可行性。