发表机构
infoteam Software AG; Qseidon GmbH（infoteam软件股份公司; Qseidon有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究基于噪声模型的伪造量子模拟器上QML模型的性能与对抗鲁棒性，通过双阶段实验发现50/50对抗重训练比率对4量子比特分类器的实际鲁棒性至关重要。
AI 中文摘要
本文研究了部署在基于噪声模型的伪造模拟器上的量子机器学习（QML）模型的性能可扩展性和对抗性鲁棒性。我们进行了双阶段研究，首先基准测试了Qiskit的Aer模拟引擎在不同硬件架构下的计算吞吐量，其次评估了在现实噪声条件下投影梯度下降（PGD）攻击和对抗性重训练策略的有效性。我们的结果量化了中等规模模拟（预计最多8个量子比特）的运行时权衡和扩展行为，并证明在现实噪声条件下，高对抗性到良性重训练比率（50/50）对于实现4量子比特分类器的实际模型鲁棒性至关重要。
英文摘要
This paper investigates the performance scalability and adversarial robustness of Quantum Machine Learning (QML) models deployed on noise-model-based fake simulators. We conduct a dual-phased study, first benchmarking the computational throughput of Qiskit's Aer simulation engine across varying hardware architectures, and second, evaluating the effectiveness of Projected Gradient Descent (PGD) attacks and adversarial retraining strategies under realistic noise conditions. Our results quantify the runtime tradeoffs and scaling behavior for medium-scale simulations (projected up to 8 qubits) and demonstrate that high adversarial-to-benign retraining ratios (50/50) are essential for achieving practical model robustness for 4-qubit classifiers under realistic noise conditions.
Comments10 pages, 3 figures
