Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching

Quantum Physics arXiv:2609.13549 (quant-ph) [Submitted on 11 Sep 2026] Title:Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching Authors:Stefano Markidis View a PDF of the paper titled Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching, by Stefano Markidis View PDF HTML (experimental) Abstract:Classical-to-quantum I/O is a critical bottleneck for data-intensive quantum workloads.

Quantum Oracle Sketching (QOS) has been proposed to address this problem. We identify its practical requirements, including the representation of data as a probability distribution over oracle addresses, the relation between sample count, approximation error, and update cost, and the coherence and runtime support required for online execution. To study QOS on current quantum computing devices, we develop an offline realization that compiles sample blocks into empirical phase oracles and evaluate it using DNA fingerprinting. The application converges with relatively few samples, while exact phase fusion combines repeated updates to the same address. However, the compiled circuits remain too deep for current quantum computing devices. Practical QOS therefore requires lower depth oracle synthesis and interactive quantum runtimes. Comments: Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET) Cite as: arXiv:2609.13549 [quant-ph] (or arXiv:2609.13549v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.13549 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Stefano Markidis Prof. [view email] [v1] Fri, 11 Sep 2026 21:30:25 UTC (273 KB) Full-text links: Access Paper: View a PDF of the paper titled Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching, by Stefano MarkidisView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-09 Change to browse by: cs cs.ET 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?)