HarmQ: Harmonic Backdoor Attacks Against Quantum Neural Networks

Abstract

Quantum Neural Networks (QNNs) have emerged as a promising paradigm for quantum machine learning in the Noisy Intermediate-Scale Quantum (NISQ) era, leveraging quantum phenomena such as superposition and entanglement to process information in exponentially large Hilbert spaces. However, QNNs inherit critical security vulnerabilities from classical neural networks, particularly susceptibility to backdoor attacks. Existing attack methods designed for classical systems fail against QNNs due to quantum-specific constraints: aggressive downsampling required by limited qubit resources destroys conventional triggers, while the spectral learning bias of parameterized quantum circuits (PQCs) restricts learnable patterns. To tackle this, we present HarmQ, a quantum-native backdoor attack that exploits PQCs' inherent Fourier decomposition bias through harmonic trigger patterns. Our approach employs sinusoidal perturbations on coarse grids with block-uniform structure, ensuring survival through downsampling while aligning with PQCs' preference for low-frequency components. This enables effective backdoor injection under realistic black-box conditions where attackers access only training data. Experiments on MNIST and Fashion-MNIST demonstrate that HarmQ achieves attack success rates exceeding 99% while maintaining over 90% clean accuracy, significantly outperforming existing methods including BadNets (2.77% ASR), Watermark (7.96% ASR), Q-FGSM (44.32% ASR) and QUAP (3.40% ASR). Parametric t-SNE visualizations of quantum state representations confirm that harmonic triggers create distinctly separated clusters, evidencing HarmQ as a fundamental security threat for QNNs.