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Quantum encodings that preserve persistent homology

arXiv Quantum Physics
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⚡ Quantum Brief
Researchers Arthur J. Parzygnat and Andrew Vlasic propose a novel quantum approach to topological data analysis (TDA) that directly encodes classical datasets into quantum states while preserving persistent homology. The study addresses a key challenge: classical-to-quantum transformations often distort topological invariants, undermining TDA’s accuracy in inferring manifold structures from point clouds. Unlike conventional quantum TDA methods that start with resource-intensive simplicial complexes, this work bypasses intermediate steps by encoding raw data directly into quantum systems. The authors identify admissible quantum encodings that maintain topological features, potentially enabling faster quantum computation of invariants like persistent homology. Published in May 2026, the paper bridges quantum computing, computational geometry, and algebraic topology, offering a foundation for more efficient quantum-enhanced TDA.
Quantum encodings that preserve persistent homology

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Quantum Physics arXiv:2605.28927 (quant-ph) [Submitted on 27 May 2026] Title:Quantum encodings that preserve persistent homology Authors:Arthur J. Parzygnat, Andrew Vlasic View a PDF of the paper titled Quantum encodings that preserve persistent homology, by Arthur J. Parzygnat and 1 other authors View PDF Abstract:Given a data set with a notion of distance, such as a point cloud in Euclidean space, topological data analysis (TDA) uses techniques from algebraic topology and metric geometry to infer the topology of a hypothetical manifold from which the data are sampled. This inference is achieved by calculating topological invariants, some of which are difficult to compute classically. Meanwhile, quantum TDA utilizes quantum processes to extract the invariants used in making such inferences in an attempt to speed up the computations. Because applying transformations to the original classical dataset could alter the associated topological invariants, we investigate which quantum encodings would best preserve the invariants of the original dataset. This line of inquiry is distinct from standard approaches in quantum TDA, whose typical starting point is not from the classical dataset directly, but rather from the associated combinatorial objects, such as simplicial complexes, which typically demand a lot of resources to construct. We take the first step at a more direct approach by focusing on which quantum encodings acting directly on the data are admissible for applying quantum algorithms to extract topological features from classical datasets. Comments: Subjects: Quantum Physics (quant-ph); Computational Geometry (cs.CG); Algebraic Topology (math.AT) Cite as: arXiv:2605.28927 [quant-ph] (or arXiv:2605.28927v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.28927 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arthur Parzygnat [view email] [v1] Wed, 27 May 2026 18:00:00 UTC (3,734 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum encodings that preserve persistent homology, by Arthur J. Parzygnat and 1 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-05 Change to browse by: cs cs.CG math math.AT 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?) 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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Source: arXiv Quantum Physics