QUFIG: GNN-Based Prediction of Quantum Fault Injection Vulnerabilities with Gate-Level Precision

2026-10-01

AI Summary

This study addresses the limitations of existing fault injection attack methods on quantum cloud platforms, which struggle to achieve gate-level vulnerability identification and adapt to dynamic environments. To this end, we propose a graph neural network framework based on circuit directed acyclic graphs (DAGs). This approach reformulates gate-level fault analysis as a learning-guided prioritization problem under a constrained fidelity budget, ranking quantum gates by predicting their vulnerability scores to enable, for the first time, the precise localization of high-impact gate-fault pairs. Evaluated on benchmarks such as QASMbench, the proposed method reduces the number of inspected gates by 2.9%–19.8% compared to random and depth-based heuristic baselines while preserving effective fault identification capabilities. These results demonstrate that our framework significantly enhances the efficiency of security assessments for quantum cloud platforms.

Abstract

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.

Problem

• quantum fault injection
• vulnerability identification
• gate-level precision
• cloud-based quantum computing
• quantum circuit reliability

Innovation

• Graph Neural Network
• Quantum Fault Injection
• Gate-Level Vulnerability
• Circuit-DAG
• Fidelity Budget