Metraipolis › archive › dc_fc7320dff487

Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access — arXiv:2609.05702

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05702
License (as archived): CC0-1.0
Archived: 2026-10-16T13:58:41.606Z
SHA-256 of the archived text: 31632099b67e7af4974a5e761876312ef7fd20f6463991014bd89973aee3d91e
Stable URL of this page: https://metraipolis.com/documento/dc_fc7320dff487

How to cite

Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access — arXiv:2609.05702 — arXiv (en), https://arxiv.org/abs/2609.05702 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc7320dff487 (Metraipolis archive dc_fc7320dff487, sha256 31632099b67e7af4974a5e761876312ef7fd20f6463991014bd89973aee3d91e)

BibTeX

@misc{dcfc7320dff487,
  title = {Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access — arXiv:2609.05702},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05702},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc7320dff487; sha256 31632099b67e7af4974a5e761876312ef7fd20f6463991014bd89973aee3d91e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05702. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 abilities and access to quantum information output from service providers. In this paper, we aim to bridge this gap by examining membership inference attacks against QML models by demonstrating that increasing quantum access and quantum computing abilities provides provable theoretical privacy leakage and empirical adversarial gain. However, the probabilistic nature of QML introduces a gap between theoretical and empirical adversarial advantage. These results show that existing research on privacy leakage in QML models underestimates privacy leakage in emergent quantum-native access regimes, and we hope to establish a first step in examining potential privacy leakages for QML in the quantum-native world.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.LG).
Categorie arXiv: Quantum Physics (quant-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.05702 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:39:43.124Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).