Late Breaking Results: Extracting QNNs from NISQ Computers via Ensemble Learning

Zhenxiao Fu, Fan Chen

Abstract: The recent success of Quantum Neural Networks (QNNs) prompts model extraction attacks on cloud platforms, even under black-box constraints. These attacks repeatedly query the victim QNN with malicious inputs. However, existing extraction attacks tailored for classical models yield local substitute QNNs with limited performance due to NISQ computer noise. Drawing from bagging-based ensemble learning, which uses independent weak learners to learn from noisy data, we introduce a novel QNN extraction approach. Our experimental results show this quantum ensemble learning approach improves local QNN accuracy by up to 15.09% compared to previous techniques.