  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.
