The Quantum Approximate Optimization Algorithm (QAOA) was proposed as a way of finding good, approximate solutions to hard combinatorial optimization problems. QAOA uses a hybrid approach. A parametrized quantum state is repeatedly prepared and measured on a quantum computer to estimate its average energy. Then, a classical optimizer, running in a classical computer, uses such information to decide on the new parameters that are then provided to the quantum computer. This process is iterated until some convergence criteria are met. Theoretically, almost all classical minimizers can be used in the hybrid scheme. However, their behaviour can vary greatly in both the quality of the final solution and the time they take to find it.

In this work, we study the performance of twelve different classical optimizers when used with QAOA to solve the maximum cut problem in graphs. We conduct a thorough set of tests on a quantum simulator both, with and without noise, and present results that show that some optimizers can be hundreds of times more efficient than others in some cases.

提出了一种量子近似优化算法（QAOA），它可以为硬组合优化问题找到良好的近似解。QAOA使用混合方法。重复准备参数化的量子态，并在量子计算机上对其进行测量，以估计其平均能量。然后，在经典计算机中运行的经典优化器使用此类信息来确定新参数，然后将其提供给量子计算机。重复此过程，直到满足某些收敛标准为止。从理论上讲，几乎所有经典的最小化器都可以在混合方案中使用。但是，他们的行为在最终解决方案的质量和找到解决方案所花费的时间上都可能有很大差异。

在这项工作中，我们研究了十二种不同的经典优化器与QAOA一起使用时的性能，以解决图形中的最大割问题。我们在有或没有噪声的情况下在量子模拟器上进行了全面的测试，并给出了结果，表明某些优化器在某些情况下的效率可能比其他优化器高数百倍。