Computational phase transitions: benchmarking Ising machines and quantum optimisers

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, teachability 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.