I doubt anytime soon (at least as part of current SAT solvers). A big barrier to applying ML to the process of SAT solving is that a lot of times, it's just faster to do a search with a simple heuristic for variable selection than try a much more time consuming ML method (and neural networks will be quite time consuming relative to the kinds of heuristics usually used) to do variable selection better.
Quantum is a special case really as DFT computation is already very time consuming.
Only ones I've seen try and predict the satisfiability directly. But usually in SAT, you're either interested in a solution or a proof of infeasibility. Prediction can't do the latter and afaik, existing non-ML based SAT solvers are far better at doing the former.
Quantum is a special case really as DFT computation is already very time consuming.