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When Does Quantum Differential Privacy Compose?

Daniel Alabi, Theshani Nuradha
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⚡ Quantum Brief
Researchers Daniel Alabi and Theshani Nuradha prove classical differential privacy composition fails in quantum systems, showing even perfectly private quantum channels can lose privacy when combined under correlated implementations. They identify a key exception: tensor-product quantum channels acting on product neighboring inputs, where composition guarantees can be restored using a quantum moments accountant framework. The team introduces an operator-valued privacy-loss metric and matrix moment-generating function, enabling Rényi-type divergence bounds despite lacking a data-processing inequality. Their approach yields advanced-composition-style bounds matching classical theory’s leading-order behavior, offering operational privacy guarantees against arbitrary quantum measurements. The work establishes that meaningful quantum privacy composition requires strict structural assumptions on channels, inputs, and adversarial measurements, clarifying classical-to-quantum theory limits.
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Quantum Physics arXiv:2601.00337 (quant-ph) [Submitted on 1 Jan 2026] Title:When Does Quantum Differential Privacy Compose? Authors:Daniel Alabi, Theshani Nuradha View a PDF of the paper titled When Does Quantum Differential Privacy Compose?, by Daniel Alabi and 1 other authors View PDF HTML (experimental) Abstract:Composition is a cornerstone of classical differential privacy, enabling strong end-to-end guarantees for complex algorithms through composition theorems (e.g., basic and advanced). In the quantum setting, however, privacy is defined operationally against arbitrary measurements, and classical composition arguments based on scalar privacy-loss random variables no longer apply. As a result, it has remained unclear when meaningful composition guarantees can be obtained for quantum differential privacy (QDP). In this work, we clarify both the limitations and possibilities of composition in the quantum setting. We first show that classical-style composition fails in full generality for POVM-based approximate QDP: even quantum channels that are individually perfectly private can completely lose privacy when combined through correlated joint implementations. We then identify a setting in which clean composition guarantees can be restored. For tensor-product channels acting on product neighboring inputs, we introduce a quantum moments accountant based on an operator-valued notion of privacy loss and a matrix moment-generating function. Although the resulting Rényi-type divergence does not satisfy a data-processing inequality, we prove that controlling its moments suffices to bound measured Rényi divergence, yielding operational privacy guarantees against arbitrary measurements. This leads to advanced-composition-style bounds with the same leading-order behavior as in the classical theory. Our results demonstrate that meaningful composition theorems for quantum differential privacy require carefully articulated structural assumptions on channels, inputs, and adversarial measurements, and provide a principled framework for understanding which classical ideas do and do not extend to the quantum setting. Comments: Subjects: Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Information Theory (cs.IT) Cite as: arXiv:2601.00337 [quant-ph] (or arXiv:2601.00337v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.00337 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Daniel Alabi [view email] [v1] Thu, 1 Jan 2026 13:24:09 UTC (32 KB) Full-text links: Access Paper: View a PDF of the paper titled When Does Quantum Differential Privacy Compose?, by Daniel Alabi and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 Change to browse by: cs cs.CR cs.IT math math.IT References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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