# Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization

arXiv: https://arxiv.org/abs/2002.04676 · HTML: https://mlsanity.com/paper/2002.04676/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration](https://mlsanity.com/paper/2205.14105/) (2022)
- [Policy-Based Self-Competition for Planning Problems](https://mlsanity.com/paper/2306.04403/) (2023)
- [Neural Simulated Annealing](https://mlsanity.com/paper/2203.02201/) (2022)
- [Compilation of product-formula Hamiltonian simulation via reinforcement learning](https://mlsanity.com/paper/2311.04285/) (2023)

## Abstract (arXiv)

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.