While there are various approaches to benchmark physical processors, recent findings have focused on computational phase transitions. This is due to several factors. Importantly, the hardest instances appear to be well-concentrated in a narrow region, with a control parameter allowing uniform random distributions of problem instances with similar computational challenge. It has been established that one could observe a computational phase transition in a distribution produced from coherent Ising machine(s). In terms of quantum approximate optimisation, the ability for the quantum algorithm to function depends critically on the ratio of a problems constraint to variable ratio (called density). The critical density dependence on performance resulted in what was called, reachability deficits. In this perspective we recall the background needed to understand how to apply computational phase transitions in various bench-marking tasks and we survey several such contemporary findings.

虽然有多种方法可以对物理处理器进行基准测试，但最近的研究结果集中在计算相变上。这是由几个因素造成的。重要的是，最难的实例似乎集中在一个狭窄的区域，控制参数允许具有类似计算挑战的问题实例的均匀随机分布。已经确定可以观察到相干伊辛机产生的分布中的计算相变。在量子近似优化方面，量子算法运行的能力关键取决于问题约束与可变比率（称为密度）的比率。对性能的临界密度依赖性导致了所谓的可达性缺陷. 从这个角度来看，我们回顾了理解如何在各种基准任务中应用计算相变所需的背景，我们调查了几个这样的当代发现。