Hardware-Tailored Resource Estimation for Magic-State Distillation on Silicon Spin Qubits
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Quantum Physics arXiv:2605.28936 (quant-ph) [Submitted on 27 May 2026] Title:Hardware-Tailored Resource Estimation for Magic-State Distillation on Silicon Spin Qubits Authors:Songqinghao Yang, Christopher K. Long, Rubén M. Otxoa, Prakash Murali, Crispin H. W. Barnes, David R. M. Arvidsson-Shukur View a PDF of the paper titled Hardware-Tailored Resource Estimation for Magic-State Distillation on Silicon Spin Qubits, by Songqinghao Yang and 5 other authors View PDF HTML (experimental) Abstract:We present a resource analysis for generating high-fidelity logical magic states on silicon spin-qubit platforms. We consider a range of architectures, including a shuttling-based SpinBus design, a dense nearest-neighbor layout, and a hybrid scheme with shuttling-connected patches. We compare surface, color, and biased error-correcting codes, and analyze the $5\to1$ and $15\to1$ magic-state distillation protocols. Our approach combines bottom-up and top-down methodologies. We construct a hardware-level noise model based on a silicon-processor Hamiltonian with realistic parameters and $1/f$ non-Markovian noise, enabling estimation of physical resources required to reach target logical error rates. These results are propagated to system-level overheads for applications including spin dynamics, integer factorization, and quantum chemistry. Conversely, we fix target logical fidelities and derive corresponding constraints on hardware performance. Our framework enables systematic evaluation of resource-reduction strategies. We find that optimized control pulses reduce magic-state distillation overhead by 42\% compared to standard gate implementations. In addition, silicon-tailored biased error-correcting codes achieve an approximately threefold reduction in physical footprint relative to the surface code, even without physical-bias-preserving operations. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.28936 [quant-ph] (or arXiv:2605.28936v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.28936 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Songqinghao Yang [view email] [v1] Wed, 27 May 2026 18:00:02 UTC (8,585 KB) Full-text links: Access Paper: View a PDF of the paper titled Hardware-Tailored Resource Estimation for Magic-State Distillation on Silicon Spin Qubits, by Songqinghao Yang and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 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?)