QTROJAN: A Circuit Backdoor Against Quantum Neural Networks
Cheng Chu, Lei Jiang, Martin Swany, Fan Chen
Abstract
We propose a circuit-level backdoor attack, QTrojan, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by a few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA) since it does not embed any trigger in the inputs of the victim QNN or require access to original training datasets. Compared to a DPBA, QTrojan improves the clean data accuracy by 21% and the attack success rate by 19.9%.
Topics & Concepts
BackdoorQuantumComputer scienceArtificial neural networkAlgorithmArtificial intelligenceComputer securityPhysicsQuantum mechanicsAdvancements in Semiconductor Devices and Circuit DesignQuantum Computing Algorithms and ArchitectureRadiation Effects in Electronics