论文标题
计算相变：基准测试机器和量子优化器
Computational Phase Transitions: Benchmarking Ising Machines and Quantum Optimisers
论文作者
论文摘要
尽管有多种基准物理处理器的方法，但最近的发现集中在计算相变。这是由于几个因素。重要的是，最困难的实例似乎在狭窄的区域中得到了充分的浓缩，其中控制参数允许对问题实例进行统一的随机分布，并具有相似的计算挑战。已经确定，可以观察到来自连贯的Ising机器产生的分布中的计算相变。就量子近似优化而言，量子算法功能的能力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所谓的可及性缺陷。从这个角度来看，我们回想起需要了解如何在各种基准标记任务中应用计算相变的背景，并调查了一些这样的当代发现。
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.
