Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning

ArXi:2605.10954v1 Announce Type: cross Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations remains a major challenge for practical deployment. In QML systems, small perturbations applied to classical inputs can propagate through the quantum encoding stage and distort the resulting quantum state, thereby degrading model performance.