Dual-regularized nonlinear quantum encoding for adversarial robustness in quantum machine learning

Abstract Quantum machine learning (QML) models are highly vulnerable to adversarial attacks, severely limiting their deployment in security-sensitive applications. To enhance their robustness, this study proposes a novel method, termed nonlinear quantum encoding with dual regularisation (NQE-DR), which combines nonlinear quantum encoding with dual regularisation. NQE-DR integrates classical nonlinear transformations, adaptive parameter optimisation, and a multi-qubit cross-coupling architecture. Our dual regularisation incorporates parameter and gradient regularisation, providing a theoretical framework to guarantee robustness under adversarial attacks and noisy conditions. Experimental results demonstrate that NQE-DR significantly outperforms mainstream encoding methods, exhibiting a notable improvement in classification accuracy and enhanced stability under FGSM attacks and noise. This study advances adversarial quantum machine learning by seamlessly integrating nonlinear encoding with a comprehensive dual regularisation strategy, offering a potent security enhancement for practical QML deployment.