aiqbench analysis
This research establishes a rigorous mathematical framework for quantum differential privacy (QDP) by characterizing the order of informativeness between quantum states. The findings provide tight upper-bounds on quantum Fisher information and near-optimal contraction bounds for private quantum channels, directly addressing limits in privatized hypothesis testing and parameter estimation. These results are critical for advancing secure quantum machine learning protocols where data utility must be preserved against privacy constraints.
Quantum Information Ordering and Differential Privacy
arXiv:2511.01467v4 Announce Type: replace-cross Abstract: We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In particular, we show that if the hypothesis testing divergence of the one pair dominates over that of the other pair, then this dominance holds for every $f$-divergence. This approach completely characterizes $(\varepsilon,\delta)$-QDP mechanisms by identifying the most informative $(\varepsilon,\delta)$-DP quantum state pairs. We apply this to study precise limits for privatized hypothesis testing and privatized quantum parameter estimation, including tight upper-bounds on the quantum Fisher information under QDP. Finally, we establish near-optimal contraction bounds for differentially private quantum channels with respect to the Hockey-Stick divergence.
Abstract:We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In particular, we show that if the hypothesis testing divergence of the one pair dominates over that of the other pair, then this dominance holds for every $f$-divergence. This approach completely characterizes $(\varepsilon,\delta)$-QDP mechanisms by identifying the most informative $(\varepsilon,\delta)$-DP quantum state pairs. We apply this to study precise limits for privatized hypothesis testing and privatized quantum parameter estimation, including tight upper-bounds on the quantum Fisher information under QDP. Finally, we establish near-optimal contraction bounds for differentially private quantum channels with respect to the Hockey-Stick divergence.
38 pages, 3 figures; Significant revision: Includes a correction to the lower bound in Lemma 5, a new technical fact (Fact 13), and an expanded Lemma 1. Introduction substantially rewritten; terminology updated; references added
Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG)
arXiv:2511.01467 [quant-ph]
(or arXiv:2511.01467v4 [quant-ph] for this version)
https://doi.org/10.1109/TIT.2026.3737562 DOI(s) linking to related resources
Full text under the publisher's open licence · 164 words
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