Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, traditional model extraction techniques designed for classical machine learning models encounter challenges when applied to NISQ computers due to significant noise in current devices. In this paper, we introduce QuantumLeak, an effective and accurate QNN model extraction technique from cloud-based NISQ machines. Compared to existing classical model stealing techniques, QuantumLeak improves local VQC accuracy by 4.99\%$\sim$7.35\% across diverse datasets and VQC architectures.

翻译：变分量子电路（VQCs）已成为实现量子神经网络（QNNs）的强大工具，可应对各类复杂问题。经过良好训练的VQCs作为托管在云端含噪中等规模量子（NISQ）计算机上的宝贵知识产权资产，容易遭受恶意的VQC窃取攻击。然而，由于当前设备存在显著噪声，针对经典机器学习模型设计的传统模型提取技术在应用于NISQ计算机时面临挑战。本文提出QuantumLeak，一种从云端NISQ机器高效且精确提取QNN模型的技术。与现有经典模型窃取技术相比，QuantumLeak在多种数据集和VQC架构上将本地VQC准确率提升了4.99\%$\sim$7.35\%。