Probing and Enhancing the Robustness of GNN-based QEC Decoders with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework to identify vulnerabilities in GNN-based quantum error correction decoders and demonstrates how adversarial training can improve their robustness.

Contribution

It presents a novel RL-based method to systematically probe and enhance the robustness of GNN decoders for quantum error correction.

Findings

RL agent successfully finds critical vulnerabilities in the decoder

Adversarial training significantly improves decoder robustness

Framework applicable to experimental quantum error correction data

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful, data-driven approach for Quantum Error Correction (QEC) decoding, capable of learning complex noise characteristics directly from syndrome data. However, the robustness of these decoders against subtle, adversarial perturbations remains a critical open question. This work introduces a novel framework to systematically probe the vulnerabilities of a GNN decoder using a reinforcement learning (RL) agent. The RL agent is trained as an adversary with the goal of finding minimal syndrome modifications that cause the decoder to misclassify. We apply this framework to a Graph Attention Network (GAT) decoder trained on experimental surface code data from Google Quantum AI. Our results show that the RL agent can successfully identify specific, critical vulnerabilities, achieving a high attack success rate with a minimal number of bit flips. Furthermore, we demonstrate that the decoder's robustness can be significantly enhanced through adversarial training, where the model is retrained on the adversarial examples generated by the RL agent. This iterative process of automated vulnerability discovery and targeted retraining presents a promising methodology for developing more reliable and robust neural network decoders for fault-tolerant quantum computing.