Quantum hardware and quantum-inspired algorithms are becoming increasingly
popular for combinatorial optimization. However, these algorithms may require
careful hyperparameter tuning for each problem instance. We use a reinforcement
learning agent in conjunction with a quantum-inspired algorithm to solve the
Ising energy minimization problem, which is equivalent to the Maximum Cut
problem. The agent controls the algorithm by tuning one of its parameters with
the goal of improving recently seen solutions. We propose a new Rescaled Ranked
Reward (R3) method that enables stable single-player version of self-play
training that helps the agent to escape local optima. The training on any
problem instance can be accelerated by applying transfer learning from an agent
trained on randomly generated problems. Our approach allows sampling
high-quality solutions to the Ising problem with high probability and
outperforms both baseline heuristics and a black-box hyperparameter
optimization approach.

本文提出了一种基于量子启发式算法 + 强化学习的方法，在解决 Ising 能量最小化问题方面，该方法通过调整其中一个参数以改进最近看到的解，并使用一种新的 Rescaled Rank Reward (R3) 方法来提高稳定的自我博弈训练效果。训练后可在任何问题实例中采样高质量的解，并优于基线启发式和黑盒超参数优化方法。