Ryota Ikeda

Ryota Ikeda

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.

Ryota Ikeda

The performance of decoders in Quantum Error Correction (QEC) is key to realizing practical quantum computers. In recent years, Graph Neural Networks (GNNs) have emerged as a promising approach, but their training methodologies are not yet well-established. It is generally expected that transferring theoretical knowledge from classical algorithms like Minimum Weight Perfect Matching (MWPM) to GNNs, a technique known as knowledge distillation, can effectively improve performance. In this work, we test this hypothesis by rigorously comparing two models based on a Graph Attention Network (GAT) architecture that incorporates temporal information as node features. The first is a purely data-driven model (baseline) trained only on ground-truth labels, while the second incorporates a knowledge distillation loss based on the theoretical error probabilities from MWPM. Using public experimental data from Google, our evaluation reveals that while the final test accuracy of the knowledge distillation model was nearly identical to the baseline, its training loss converged more slowly, and the training time increased by a factor of approximately five. This result suggests that modern GNN architectures possess a high capacity to efficiently learn complex error correlations directly from real hardware data, without guidance from approximate theoretical models.