Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization
This work addresses the need for efficient hyperparameter tuning in quantum-inspired optimization algorithms, offering a domain-specific improvement for combinatorial problems like Ising energy minimization.
The paper tackles the problem of hyperparameter tuning in quantum-inspired algorithms for combinatorial optimization by using a reinforcement learning agent to control algorithm parameters, achieving high-quality solutions that outperform baseline heuristics and black-box optimization methods.
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.