A Delegated Quantum Approximate Optimization Algorithm

Abstract

The famous quantum approximate optimization algorithm (QAOA) can be an efficient way to solve combinatorial optimization problems which are very useful for various applications such as network analysis and image segmentation. However, its implementation needs a quantum computer. Fortunately, blind quantum computing (BQC) can allow a user with limited quantum ability to delegate the computation to a quantum server and still keep his input, algorithm and output private. In this paper, we improve one typical BQC model for users who only need the ability to perform single-qubit measurements by utilizing an efficient way to generate quantum resource states and thus reduce the quantum memory required by the quantum server. Then by combining the improved BQC model and QAOA, we propose a delegated QAOA where users only with the ability to implement single-qubit measurements also can complete QAOA with the help of a remote quantum server while ensuring the security of their own data.