Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accompanying QML is also starting to be studied, which inherit privacy leakage channels from "classical" ML and also quantum-unique risks. Existing work on privacy-preserving QML largely focuses on a QML-as-a-service scenario, which generally assumes that the QML model owner provides only classical bit outputs to queries, while users (and adversaries) have only classical computing abilities. However, this view is increasingly challenged in a quantum-native world of quantum-capable users/adversaries, which may have access to both quantum computing ...
| Authors | Liou Tang, James Joshi, Ashish Kundu |
|---|---|
| Primary category | quant-ph |
| Published | 4 September 2026 |
| Last updated at source | 9 September 2026 |
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