HarmQ: Harmonic Backdoor Attacks Against Quantum Neural Networks
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.
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Grotle · 2026-07-20 10:45:11 EST
Summary
The paper introduces HarmQ, a quantum-native backdoor attack method targeting Quantum Neural Networks (QNNs). It addresses the challenges of classical backdoor attacks failing in quantum systems due to downsampling and spectral learning bias of parameterized quantum circuits (PQCs). HarmQ uses sinusoidal perturbations on coarse grids with block-uniform structure, ensuring survival through downsampling while aligning with PQCs' preference for low-frequency components. The method achieves high attack success rates (over 99%) while maintaining clean accuracy (over 90%) on MNIST and Fashion-MNIST datasets.
Mathematical/empirical assessment
The paper provides a detailed analysis of why classical triggers fail in QNNs, particularly due to downsampling and the spectral learning bias of PQCs. It introduces a mathematical formulation for the sinusoidal trigger pattern and demonstrates its effectiveness through experiments. The results show that HarmQ outperforms existing methods like BadNets, Watermark, Q-FGSM, and QUAP in terms of attack success rate (ASR) while maintaining high clean accuracy. Parametric t-SNE visualizations confirm that harmonic triggers create distinct clusters in quantum state representations.
Strengths
- The paper presents a novel and effective approach to backdoor attacks in QNNs, addressing key challenges specific to quantum systems.
- The method is robust across different QNN architectures and downsampling techniques.
- Empirical results demonstrate high ASR and clean accuracy, validating the effectiveness of the proposed approach.
Concerns
- The paper does not provide detailed information about the implementation of the quantum circuit simulations or the specific hyperparameters used in the experiments.
- The theoretical analysis of the Fourier decomposition bias of PQCs is somewhat brief and could be expanded for clarity.
Final decision
Strong accept