Adaptive Shot Allocation for Recursive QAOA via Reinforcement Learning
Recursive QAOA (RQAOA) solves combinatorial optimization problems by using shallow quantum circuits to estimate pairwise correlations and recursively eliminate variables until a classical solver can handle the residual instance. Each elimination step requires measurement shots, and the total shot cost grows with the number of recursive stages. On near-term quantum devices, increasing shot counts can translate directly into greater exposure to hardware-level noise sources such as readout errors and decoherence, making shot-efficient execution not merely a cost-reduction measure but a factor with direct implications for solution reliability. While shot reduction has been studied broadly across NISQ algorithms, step-wise measurement control inside the recursive loop of RQAOA has received little attention. We formulate this step-wise allocation as a sequential decision problem and propose two strategies for depth-1 RQAOA on weighted Max-Cut instances. A hand-crafted heuristic assigns shots based on local indicators of step difficulty, and a tabular Double Q-learning agent learns a residual policy that adjusts this baseline under a Lagrangian-constrained objective. Both methods are evaluated under a fixed-cap fairness protocol that equalizes the per-step budget across all strategies, and the elimination rule itself is kept unchanged so that the contribution of adaptive measurement control can be isolated. On a diverse set of weighted graph instances spanning a range of sizes and structures, the heuristic reduces total shots by approximately 23% relative to uniform allocation, and the RL policy achieves a 36% reduction with a lower effective shots per success ratio than both baselines. The improvement persists on problem sizes not seen during training, suggesting that reinforcement learning can discover efficient, instance-adaptive measurement strategies in recursive quantum optimization.