I'm currently trying to work on a minimization problem using D-Wave hybrid and quantum machines.
The problem is, shortly, an energy optimization for a network of switches and servers, linked as a binary tree, with the servers as leaves. The tree is tested with various depth, but the problems I'll list below show as soon as a depth of 2 or 3.
Being new on the SDK (and in Quantum Computing in general) I'm having some difficulties on the resolution of it, 2 things in particular:
- Apparently the problem is too large to be computed by the full quantum machine, so I've been directed towards a decomposer, which should decompose the problem in a way it becomes solvable. I've trying to read the docs but the examples are a bit vague and when i try to apply them to other problems besides the ones showed in the examples I get no real solutions. Moreover I don't understand if it takes a second phase after the decomposition and solution where I should compose the problem with the composer
- I've formulated the problem as a CQM, then converted it with the apposite function
cqm_to_bqm()to a BQM. From what I've understood from D-Wave videos and tutorials, the BQM should take less time than the CQM, instead it takes 40-50 times more and returns a way worse energy solution. Is there some hidden problem in converting from CQM to BQM or maybe is it still linked to the previous problem with too much variables to be computed efficiently?
I'm sorry if these could be silly questions, but I'm really new to this whole world, and it's not easy to learn.