Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors
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Quantum Physics arXiv:2601.08930 (quant-ph) [Submitted on 13 Jan 2026] Title:Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors Authors:Enhyeok Jang, Hyungseok Kim, Yongju Lee, Jaewon Kwon, Yipeng Huang, Won Woo Ro View a PDF of the paper titled Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors, by Enhyeok Jang and 5 other authors View PDF HTML (experimental) Abstract:A recent advancement in quantum computing shows a quantum advantage of certified randomness on the racetrack processor. This work investigates the execution efficiency of this architecture for general-purpose programs. We first explore the impact of increasing zones on runtime efficiency. Counterintuitively, our evaluations using variational programs reveal that expanding zones may degrade runtime performance under the existing scheduling policy. This degradation may be attributed to the increase in track length, which increases ion circulation overhead, offsetting the benefits of enhanced parallelism. To mitigate this, the proposed \textit{Plutarch} exploits 3 strategies: (i) unitary decomposition and translation to maximize zone utilization, (ii) prioritizing the execution of nearby gates over ion circulation, and (iii) implementing shortcuts to provide the alternative path. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.08930 [quant-ph] (or arXiv:2601.08930v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.08930 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Enhyeok Jang [view email] [v1] Tue, 13 Jan 2026 19:09:58 UTC (971 KB) Full-text links: Access Paper: View a PDF of the paper titled Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors, by Enhyeok Jang and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?)