In the field of quantum information, classical optimizers play an important role. From experimentalists optimizing their physical devices to theorists exploring variational quantum algorithms, many aspects of quantum information require the use of a classical optimizer. For this reason, there are many papers that benchmark the effectiveness of different optimizers for specific quantum optimization tasks and choices of parameterized algorithms. However, for researchers exploring new algorithms or physical devices, the insights from these studies don't necessarily translate. To address this concern, we compare the performance of classical optimizers across a series of partially-randomized tasks to more broadly sample the space of quantum optimization problems. We focus on local zeroth-order optimizers due to their generally favorable performance and query-efficiency on quantum systems. We discuss insights from these experiments that can help motivate future works to improve these optimizers for use on quantum systems.

在量子信息领域，经典优化器起着重要的作用。针对量子最优化任务和参数化算法的不同优化器的有效性进行基准测试对于研究人员来说很重要。然而，对于探索新算法或物理设备的研究人员来说，这些研究所得的见解不能直接应用。为了解决这个问题，我们通过在部分随机化任务上比较经典优化器的性能，更广泛地采样量子最优化问题的空间。我们重点关注局部零阶优化器，因为它们在量子系统上通常表现出良好的性能和查询效率。我们讨论了从这些实验中得出的见解，可以激发未来的研究来改进这些优化器在量子系统上的使用。

用于变分量子系统的局部零阶优化器的随机基准测试