  Quantum Local Search (QLS) is a promising approach that employs small-scale
quantum computers to tackle large combinatorial optimization problems through
local search on quantum hardware, starting from an initial point. However, the
random selection of the sub-problem to solve in QLS may not be efficient. In
this study, we propose a reinforcement learning (RL) based approach to train an
agent for improved subproblem selection in QLS, beyond random selection. Our
results demonstrate that the RL agent effectively enhances the average
approximation ratio of QLS on fully-connected random Ising problems, indicating
the potential of combining RL techniques with Noisy Intermediate-scale Quantum
(NISQ) algorithms. This research opens a promising direction for integrating RL
into quantum computing to enhance the performance of optimization tasks.
