Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
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. In this work, we propose a defense mechanism that replaces the conventional quantum encoding stage of a QML model with passive steering-based controlled state preparation, which guides the encoded state toward a controlled intermediate state. By tuning the steering strength ...
| Authors | Sahan Sanjaya, Hari Krishna Parvatham, Emma Andrews et al. |
|---|---|
| Primary category | quant-ph |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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