Undermining Quantum Circuit Obfuscation: Insights from Structural Analysis

The developments in quantum computing hardware and software are focused on boosting the scalability and robustness of quantum systems. With near-term quantum computers poised to accelerate specific computational tasks, ensuring their security has become a pressing priority. Recent efforts have proposed practical solutions to ensure data confidentiality in quantum computers accessed through the cloud, particularly in the presence of untrusted quantum cloud providers by leveraging trusted hardware that enables quantum circuit obfuscation. However, these solutions overlook the detailed structural information of the quantum algorithms, which may limit the effectiveness of the obfuscation methods. In this paper, we investigate the structural constraints of quantum algorithms executed across different quantum architectures. Based on this analysis, we develop a Machine Learning (ML)-based classifier that reveals the underlying quantum algorithm of the obfuscated physical quantum circuit. We also propose a pre-processing phase to minimize the obfuscation's impact on the classifier accuracy. We evaluate our proposed ML-based classification model on different obfuscated physical quantum circuits, which are transpiled to support IBM quantum hardware. Our analysis shows the vulnerability of existing obfuscation methods to reverse engineering attacks, which can potentially expose sensitive information about quantum circuits.