The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has the potential to quickly identify the structure of high-quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggests that quantum annealing’s performance on the proposed optimization tasks is distinct. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and suggests the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.

诸如量子计算机，相干伊辛机和数字退火炉等新型计算设备的最新出现为硬件加速的混合优化算法提供了新的机遇。不幸的是，利用新颖的硬件平台实现毫无疑问的性能提升的演示面临着巨大的障碍。一项关键挑战是了解将这些设备与既定的优化方法区分开的算法特性。通过精心设计人为的优化任务，这项工作为量子退火的计算属性提供了新的见解，并表明该模型具有快速识别高质量解决方案结构的潜力。与涵盖完整搜索和局部搜索的各种算法的精心比较表明，量子退火在所提出的优化任务上的性能是独特的。该结果为优化问题的时标和类型提供了新的见解，在这些问题中，量子退火有可能比已建立的优化算法显着提高性能，并建议开发结合了量子退火和最佳状态的最佳混合算法。 -艺术经典方法。