Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems.

翻译：量子近似优化算法（QAOA）是用于通过近似求解组合优化问题的流行量子算法之一。QAOA既可在经典计算机模拟的物理量子计算机上评估，也可在虚拟量子计算机上评估，后者因其无噪声特性和可用性而受到青睐。然而，在虚拟量子计算机上执行QAOA求解需要大规模量子电路模拟（QCS）的组合优化问题时，会面临模拟速度缓慢的问题。本文提出通过数学优化压缩量子操作、结合高效按位运算进一步降低计算复杂度、并利用现代多核处理器的不同层级并行性来加速QAOA的QCS技术，并通过求解最大割问题的案例研究展示其有效性。