用强化学习找图神经网络量子纠错的漏洞并提升抗干扰能力
Probing and Enhancing the Robustness of GNN-based QEC Decoders with Reinforcement Learning
- 用强化学习当对手，自动寻找让纠错模型出错的最小扰动
- 在谷歌实验数据上，仅少量比特翻转就导致纠错失败
- 通过对抗训练让模型学会抵御攻击，适合做量子计算可靠性的研究者
图神经网络（GNN）已成为一种强大的数据驱动型量子错误纠正（QEC）解码方法，能直接从偶校验数据中学习复杂的噪声特征。然而，这些解码器对细微的对抗性扰动的鲁棒性仍是关键开放问题。本文提出一种新框架，利用强化学习（RL）代理系统性地探测GNN解码器的脆弱性。该代理作为攻击者，目标是找到导致解码器误判的最小偶校验修改。我们将其应用于基于谷歌量子人工智能实验表面码数据训练的图注意力网络（GAT）解码器。结果表明，该RL代理能有效识别特定关键漏洞，在极小比特翻转下实现高成功率攻击。此外，我们证明通过对抗训练——即在RL生成的对抗样本上重新训练模型——可显著提升解码器鲁棒性。这一自动发现漏洞与针对性重训的迭代流程，为开发更可靠、鲁棒的容错量子计算神经网络解码器提供了有前景的方法。
原文摘要 · Abstract (English)
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.
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