Title: Feature-Level Adversarial Attack on Quantum Neural Networks
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: quantum neural networks, adversarial attack, quantum computing

Abstract: Quantum Neural Networks (QNNs) have recently demonstrated promising performance in various tasks by leveraging the unique advantages of quantum computing. However, recent studies have also revealed the high sensitivity of QNNs to adversarial perturbations, posing a threat to their practical applications. Existing methods are developed under idealized assumptions, neglecting key practical constraints such as the inaccessibility of exact gradients and the stochasticity of quantum measurements in near-term noisy intermediate-scale quantum (NISQ) devices, thereby limiting their practical value. In this paper, we propose QMirage, a feature-level adversarial attack against QNNs that incorporates quantum-unique properties and resolves the gradient issue. Based on the definition of quantum latent features, we first introduce a new optimization objective to search for adversarial examples in feature space. We further employ natural evolution strategies (NES) with gradient priors for unbiased gradient estimation. Moreover, dynamic adjustment for the learning rate is combined to reduce failures caused by suboptimal fixed configurations. Experiments on benchmark datasets and QNNs demonstrate that QMirage achieves more effective and efficient attacks compared to baselines, while preserving comparable visual quality. It exhibits superior robustness under finite-shot and noisy settings with acceptable measurement costs. The results also reflect the influence of the model structure and encoding on adversarial robustness, providing insights for the future design of resilient QNNs.