David Vesterlund

After porting the quantum-chemistry pipeline of a generative model writing circuits, a quantum sampler drawing bitstrings, classical repair expanding them and a classical solver picking the best, to maximum weighted independent set, a preregistered held-out test returns a negative answer: tabu search reaches repair-basin mass 0.82–0.94 and simulated annealing comes second, while every quantum sampler and a matched learned classical policy trails both at every size. An earlier positive-looking result came from picking the best of 50 configurations per instance, an upper bound rather than a deployable method; under random configurations, oracle QAOA reaches only 0.014 at n=28.

After porting the quantum-chemistry pipeline of a generative model writing circuits, a quantum sampler drawing bitstrings, classical repair expanding them and a classical solver picking the best, to maximum weighted independent set, a preregistered held-out test returns a negative answer: tabu search reaches repair-basin mass 0.82–0.94 and simulated annealing comes second, while every quantum sampler and a matched learned classical policy trails both at every size. An earlier positive-looking result came from picking the best of 50 configurations per instance, an upper bound rather than a deployable method; under random configurations, oracle QAOA reaches only 0.014 at n=28.