Iterative Quantum Optimization: From Greedy Reductions to Branch-and-Bound

arXiv:2611.00678v1 Announce Type: new Abstract: Iterative quantum optimization (IQO) uses quantum-generated information to select a sequence of classical problem reductions. We introduce Bound-Aware Tree-IQO which combines this iterative structure with bounds and reversible search. Feasible measured samples update a shared incumbent, while valid classical bounds prune branches that cannot improve it. Incorporating backtracking yields an anytime method that returns the best feasible solution found when interrupted, or can be run as a complete search to return and certify the optimum.