This paper presents a novel framework for systematically investigating vulnerabilities in graph neural network (GNN) decoders for quantum error correction (QEC) using reinforcement learning (RL) agents. The RL agent is trained as an adversary, seeking the minimal syndrome correction that causes decoder misclassification. Applying this framework to a graph attention network (GAT) decoder trained on experimental surface code data from Google Quantum AI, we demonstrate that the RL agent successfully identifies specific critical vulnerabilities with a high attack success rate and minimal bit flips. Furthermore, we demonstrate that adversarial training, which retrains the model using adversarial examples generated by the RL agent, can significantly improve the decoder's robustness. This iterative process of automated vulnerability discovery and goal-directed retraining presents a promising methodology for developing more reliable and robust neural network decoders for fault-tolerant quantum computing.
