Linear Cross-Entropy Benchmark is the framework used to try and estimate the accuracy of the random-circuit sampling -- to try and determine how likely it is th

Linear Cross-Entropy Benchmark is the framework used to try and estimate the accuracy of the random-circuit sampling -- to try and determine how likely it is that the random circuits are being executed correctly on the quantum computer. From Scott Aaron's blog post about this paper: https://www.scottaaronson.com/blog/?p=4372 https://www.scottaaronson.com/blog/?p=4372 -- he shares some thoughts on whether this benchmark is itself easy or hard to simulate on a classical computer. > Ah, but at the end of the day, we only believe that Google’s Sycamore chip is solving a classically hard problem because of the statistical test that Google applies to its outputs: the so-called “Linear Cross-Entropy Benchmark,” which I described in Q3 of my FAQ. And even if we grant that calculating the output probabilities for a random quantum circuit is almost certainly classically hard, and sampling the output distribution of a random quantum circuit is almost certainly classically hard—still, couldn’t spoofing Google’s benchmark be classically easy? He goes on to supply an argument that its a hard test to spoof classically. I'm wondering though -- has anyone addresses how hard it would be to spoof this benchmark using a collection of smaller (potentially non-scalable) quantum computers ...? E.g -- if it were possible to spoof the benchmark with a smaller quantum computer -- would that undermine the argument in the paper that this benchmark is evidence of quantum computing scaleability ...?