I couldn't disagree with you more.
The "author" you are referring to is: https://en.wikipedia.org/wiki/Gil_Kalai
He is a multi-award winner, internationally recognized researcher with several dozen publications on mathematics and computer science.
> The author raises points in contention, but they largely just seem like nitpicks to me.
I am not sure if you and I read the same post if you think that his points of contention are merely "nitpicks".
https://gilkalai.wordpress.com/2019/08/21/the-argument-again... https://gilkalai.files.wordpress.com/2019/09/main-pr.pdf https://arxiv.org/abs/1908.02499 https://gilkalai.files.wordpress.com/2019/09/cern.pptx
In the paper linked by this post, the author first very precisely defines how quantum supremacy is defined:
> if you can show that D’ is close enough to D before you reach the supremacy regime, and you can carry out the sampling in the supremacy regime then this gives you good reason to think that your experiments in the supremacy regime demonstrate “quantum supremacy”.
Author references an older paper running this experiment, brings up a very good point that there is no quantitative measurement provided of the similarity of D and D':
> The Google group itself ran this experiment for 9 qubits in 2017. One concern I have with this experiment is that I did not see quantitative data indicating how close D’ is to D.
First point of contention, doesn't seem like merely a "nitpick" and if you disagree I'd love to hear your reasoning.
> The twist in Google’s approach is that they try to compute D’ based mainly on the 1-qubit and 2-qubit (and readout errors) errors and then run an experiment on 53 qubits where they can neither compute D nor verify that they sample from D’. In fact they sample 10^7 samples from 0-1 strings of length 53 so this is probably much too sparse to distinguish between D and the uniform distribution
If they aren't sampling from D', then they can't compare it to D, and so this violates the basic definition of quantum supremacy via sampling of random circuits. The author's point about sample size being too sparse to compute the difference between D and the uniform distribution is also valid.
He made this caveat at the start
> A single run of the quantum computer gives you only one sample from D’ so to get a meaningful description of the target distribution you need to have many samples.
> What is needed is experiments to understand probability distributions obtained by pseudorandom circuits on 9-, 15-, 20-, 25- qubits. How close they are to the ideal distribution D and how robust they are (namely, what is the gap between experimental distributions for two samples obtained by two runs of the experiment.)
Seems like a big oversight to not do multiple runs and to compare the sampled D' distributions.
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