发表机构
Northeastern University; Lehigh University; University of California, Merced（东北大学; 利哈伊大学; 加州大学默塞德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
QUFIG利用电路DAG的GNN模型，在受限保真度预算下预测门级故障注入漏洞，通过排序优先检查，减少2.9%-19.8%的检查门数，提升量子电路安全性。
AI 中文摘要
量子硬件规模的扩大和可及性的提升，暴露了量子计算工作流中新的可靠性和安全性挑战，例如基于云的量子计算平台中的运行时故障注入攻击。然而，现有工作无法以门级精度识别漏洞，或适应运行时环境。在本工作中，我们将门级故障分析表述为在受限保真度预算下的学习引导优先级排序问题。该框架使用基于电路DAG的GNN骨干网络，预测每个门对每种注入故障类型的漏洞分数，该分数定义为门-故障对电路保真度的影响。随后，门-故障对按其漏洞分数进行排序。在QASMbench和HamLib MaxCut上的实验表明，与随机和基于深度的启发式方法相比，QUFIG能以更少的检查次数恢复高影响力的易受攻击门-故障对。我们的结果表明，QUFIG可以将需要检查的门数量减少2.9%至19.8%，同时保持有效的故障识别，使量子电路设计者能够更高效地识别漏洞并应用针对性防御。
英文摘要
The growing scale and accessibility of quantum hardware exposed new reliability and security challenges in the quantum computing workflow, such as the run-time fault injection attacks in cloud-based quantum computing platforms. However, existing works fail to identify vulnerabilities with gate-level precision or adapt to run-time environments. In this work, we formulate gate-level fault analysis as a learning-guided prioritization problem under restricted fidelity budgets. The framework uses a circuit-DAG-based GNN backbone to predict the vulnerability score of each gate to each type of injected fault, defined as the impact of the gate-fault pair on circuit fidelity. The gate-fault pairs are then ranked by their vulnerability score. Experiments on QASMbench and HamLib MaxCut show that QUFIG recovers high-impact vulnerable gate-fault pairs with fewer inspections than random and depth-based heuristics. Our results show that QUFIG can reduce the number of gates requiring inspection by 2.9--19.8% while maintaining effective fault identification, allowing quantum circuit designers to identify vulnerabilities and apply targeted defenses more efficiently.
CommentsAccepted by IEEE International Conference on Computer Design (ICCD) 2026
