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Instead, Mc Geogh used the time to enlighten the assembled experts about what quantum annealing was, what an exact solver was, etc.
Incredibly, she spoke for more than an hour, without once mentioning the USC results that found that simulated annealing on a standard laptop (when competently implemented) handily outperformed the D-Wave machine, or making any attempt to reconcile those results with hers and Wang’s.Without further ado, here’s what Lidar says: I don’t believe D-Wave’s approach is scalable without error correction.I believe that the incorporation of error correction is a necessary condition in order to ever achieve a speedup with D-Wave’s machines, and I don’t believe D-Wave’s machines are any different from other types of quantum information processing in this regard.Here’s an analogy: imagine that a biotech startup claimed that, by using an expensive and controversial new gene therapy, it could cure patients at a higher rate than with the best available conventional drugs—basing its claim on a single clinical trial.Imagine that this claim was widely repeated in the press as an established fact.
I left without asking questions, not wanting to be the one to instigate an unpleasant confrontation, and—I’ll admit—questioning my own sanity as a result of no one else asking about the gigantic elephant in the room. Among the many interesting comments below, see especially this one by Alex Selby, who says he’s written his own specialist solver for one class of the Mc Geoch and Wang benchmarks that significantly outperforms the software (and D-Wave machine) tested by Mc Geoch and Wang on those benchmarks—and who provides the Python code so you can try it yourself.