At the moment I believe they can only solve toy problems (like factoring the number 15 - it's apparently 3 * 5 with high probability) and/or ones that are specifically contrived to show quantum supremacy.
Is this theoretically likely to give near certain probability to the right answer on large problems or will it be affected by local minimums and what not?
Is that the issue with scale? That noise propagates through the system and renders the uplift in problem solving moot?
Not even a single logical qubit has been achieved to date, even with error correction, so it might be a bit soon to talk of factoring anything.
And then off in the corner, we've got D-Wave, who everybody loves to hate on, doing their own thing with an optimization-based approach to factoring which actually seems to work on (iirc) 10-ish bit semiprimes and zero foreknowledge.
I thought you were talking about deep learning there for a second
You may also have a high 5, that could compensate for a low value of 2.
[0]: https://ai.googleblog.com/2019/10/quantum-supremacy-using-pr...
https://www.scottaaronson.com/blog/?p=4317
> So, tl;dr, the quantum computer is simply asked to apply a random (but known) sequence of quantum operations—not because we intrinsically care about the result, but because we’re trying to prove that it can beat a classical computer at some well-defined task.