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From Scott Aaronson: > If one considers only the ~10% of instances on which the D-Wave machine does best, then the machine does do slightly better on those ins
by foobarqux 13y ago
From Scott Aaronson:
> If one considers only the ~10% of instances on which the D-Wave machine does best, then the machine does do slightly better on those instances than simulated annealing does. (Conversely, simulated annealing does better than the D-Wave machine on the ~75% of instances on which it does best.) Unfortunately, no one seems to know how to characterize the instances on which the D-Wave machine will do best: one just has to try it and see what happens! And of course, it’s extremely rare that two heuristic algorithms will succeed or fail on exactly the same set of instances: it’s much more likely that their performances will be correlated, but imperfectly. So it’s unclear, at least to me, whether this finding represents anything other than the “noise” that would inevitably occur even if one classical algorithm were pitted against another one.
He continues:
> However, I neglected to mention that even the slight “speedup” on ~10% of instances, only appears when one looks at the “quantiles of ratio”: in other words, when one plots the probability distribution of [Simulated annealing time / D-Wave time] over all instances, and then looks at (say) the ~10% of the distribution that’s best for the D-Wave machine. The slight speedup disappears when one looks at the “ratio of quantiles”: that is, when one (say) divides the amount of time that simulated annealing needs to solve its best 10% of instances, by the amount of time that the D-Wave machine needs to solve its best 10%. And Rønnow et al. give arguments in their paper that ratio of quantiles is probably the more relevant performance comparison than quantiles of ratio. (Incidentally, the slight speedup on a few instances also only appears for certain values of the parameter r, which controls how many possible settings there are for each coupling. Apparently it appears for r=1, but disappears for r=3 and r=7—thereby heightening one’s suspicion that we’re dealing with an artifact of the minimum annealing time or something like that, rather than a genuine speedup.)
- joe_the_user 13y agoThis seems like a boiler plate criticism of an earlier D-Wave. My quick read of the article gives me the impression that it and the most recent D-Wave render this criticism obsolete. Shouldn't the higher rated comment actually related to the article at hand (I'm not very qualified to evaluate it but it seems to give specific points that address these objections).
- d4vlx 13y agoScott's post is from January 16th and was updated on the 26th, as far as I understand he is using the latest results: http://www.scottaaronson.com/blog/?p=1643 http://www.scottaaronson.com/blog/?p=1643
- joe_the_user 13y agoI don't see anything in the parent quotes that addresses the discussion of the article, which goes into the relative performance of the D-Wave and custom algorithms in detail, describing situations where the D-Wave is faster; "But importantly, if you move to problems with structure, then the hardware does much better. See Figure 3. This example is intriguing from a physics perspective, since it suggests co-tunneling is helping the hardware figure out that the spins in each unit cell have to to be flipped as a block to see a lower energy state..." Which isn't a blanket endorsement but is a statement that there are places where they can show D-Wave is better (Vs Aaronson's "Unfortunately, no one seems to know how to characterize the instances on which the D-Wave machine will do best") Edit: Looking at Aaronsen's blog, he is using some very recent results but I don't see any indications that these results are the same as those referenced by the parent article here by the "Google AI Team". Maybe I'm missing something.