QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ
  Machines
Zhenxiao Fu Min Yang Cheng Chu Yilun Xu Gang Huang Fan Chen
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.