# QAOA

Variational quantum algorithms normally rely on a classical optimizer to tune gate angles, and every loss evaluation is a run of the quantum device. A study has the frontier model GPT-6 Astra write the angles directly from the problem, circuit and gate conventions, with no loss or gradient returned; on transverse-field Ising chains, H2 and depth-one QAOA its angles match the optimizer, and as a warm start they cut the median optimizer evaluations to best value down to one.

Variational quantum algorithms normally rely on a classical optimizer to tune gate angles, and every loss evaluation is a run of the quantum device. A study has the frontier model GPT-6 Astra write the angles directly from the problem, circuit and gate conventions, with no loss or gradient returned; on transverse-field Ising chains, H2 and depth-one QAOA its angles match the optimizer, and as a warm start they cut the median optimizer evaluations to best value down to one.

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.