Shiho Kim

Recursive QAOA re-measures at every variable elimination, so shot cost compounds along the recursion even though steps differ in difficulty. The work casts step-wise shot allocation in depth-1 recursive QAOA as a sequential decision problem: a hand-crafted rule keyed to local difficulty sets a baseline, and a tabular Double Q-learning agent learns residual corrections under a Lagrangian constraint while the elimination rule stays fixed. Under a fixed per-step cap, on 130 held-out policy-instance pairs the heuristic cuts total shots by about 23% and the learned policy by 36.1% versus uniform allocation, with an effective shots-per-success ratio of 0.676, and the gain holds at about 36.6% on problem sizes unseen in training.

Recursive QAOA re-measures at every variable elimination, so shot cost compounds along the recursion even though steps differ in difficulty. The work casts step-wise shot allocation in depth-1 recursive QAOA as a sequential decision problem: a hand-crafted rule keyed to local difficulty sets a baseline, and a tabular Double Q-learning agent learns residual corrections under a Lagrangian constraint while the elimination rule stays fixed. Under a fixed per-step cap, on 130 held-out policy-instance pairs the heuristic cuts total shots by about 23% and the learned policy by 36.1% versus uniform allocation, with an effective shots-per-success ratio of 0.676, and the gain holds at about 36.6% on problem sizes unseen in training.