INJEQT: Improved Magic-State Injection Protocol for Fault-Tolerant Quantum Extractor Architectures
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Quantum Physics arXiv:2604.25094 (quant-ph) [Submitted on 28 Apr 2026] Title:INJEQT: Improved Magic-State Injection Protocol for Fault-Tolerant Quantum Extractor Architectures Authors:Sayam Sethi, Sahil Khan, Aditi Awasthi, Abhinav Anand, Jonathan Mark Baker View a PDF of the paper titled INJEQT: Improved Magic-State Injection Protocol for Fault-Tolerant Quantum Extractor Architectures, by Sayam Sethi and 4 other authors View PDF HTML (experimental) Abstract:Near-term FTQC system designs are constrained by limited error budgets and largely sequential execution of non-Clifford gates. As a result, reducing the number of the most-error prone instructions becomes critical for successful program execution. In this work, we study the extractor architecture, a recently proposed FTQC design that enables universal quantum computation on spatially-efficient QEC codes such as the BB code family. In these architectures, over $90\%$ of the total program error arises from the synthillation process, which involves $\lvert T\rangle$-state preparation and injection to implement non-Clifford gates. We observe that standard Rz synthillation requires multiple sequential $\lvert T\rangle$-state injections, each incurring an inter-module measurements, the most expensive instruction in the architecture, which cumulatively dominate the overall error budget. To address this bottleneck, we propose INJEQT, a $2$-factory design that uses an auxiliary code capable of synthesizing $Rz(\theta)$ states with lower error rates. These states are then injected into the extractor modules using only a constant number of inter-module measurements. This approach reduces overall error rates by up to $22\times$. We further reduce the time overhead by a pre-fetching strategy that prepares the Rz states and their correction states in parallel. This approach improves the wall-clock time by up to $13\times$ and reduces the space-time cost by up to $7.2\times$, for an optimal choice of the number of INJEQT factories for each metric. We evaluate INJEQT for multiple state preparation techniques such as distillation, cultivation and STAR, and model the execution times for both lattice surgery-based and transversal CNOT based injections. Our results demonstrate that INJEQT is robust across factory choices and device technologies, enabling more efficient architectural designs for FTQC. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.25094 [quant-ph] (or arXiv:2604.25094v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.25094 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sayam Sethi [view email] [v1] Tue, 28 Apr 2026 00:58:22 UTC (1,264 KB) Full-text links: Access Paper: View a PDF of the paper titled INJEQT: Improved Magic-State Injection Protocol for Fault-Tolerant Quantum Extractor Architectures, by Sayam Sethi and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 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?)