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Published 20 hours ago • verified publisher icon bqth29

SB Parameters fine-tuning

Open bqth29 opened this issue 1 month ago • 0 comments

Currently, the parameters of the SB algorithm are hardcoded. Though they work quite well on unconstrained problems, we noticed a huge performance drop on constrained problems converted to QUBO/Ising formulations.

The purpose of this issue is to start a discussion and share ideas on how these parameters could be made scalable. First ideas include pre-determined sets of parameters for given problem typologies, machine learning for fine-tuning, grid-searches, ...

All help is welcome!

bqth29 avatar Oct 08 '25 10:10 bqth29