StreamingQEC: Streaming Quantum Error Correction in Tightly Integrated Quantum-Classical Systems via Certified Recurrence

Quantum Physics arXiv:2607.13351 (quant-ph) [Submitted on 15 Jul 2026] Title:StreamingQEC: Streaming Quantum Error Correction in Tightly Integrated Quantum-Classical Systems via Certified Recurrence Authors:Panayiotis Christou, Shuwen Kan, Hao Wang, Ying Mao View a PDF of the paper titled StreamingQEC: Streaming Quantum Error Correction in Tightly Integrated Quantum-Classical Systems via Certified Recurrence, by Panayiotis Christou and 3 other authors View PDF HTML (experimental) Abstract:Fault-tolerant quantum computing requires a continuous hybrid quantum error correction (QEC) pipeline comprising measurement readout, syndrome transport, decoding, feedback, and control. Existing QEC simulators primarily evaluate circuits, noise models, decoders, and protocol-level outcomes. System architects, however, must also understand how these workloads contend for and queue across controller, compute, accelerator, and communication resources during protected logical execution. We introduce StreamingQEC, a system-level simulator that translates fault-tolerant logical workloads into resource-constrained streaming-QEC pipelines. An explicit discrete-event simulation provides the reference execution semantics. An automatic staged-fluid mode enables faster approximate design-space exploration, while a certified recurrence mechanism compresses repeated transitions only when their scheduling state and metric contributions match those of the explicit execution trace. We assemble a decoder-runtime dataset containing 9,998 measurements, of which 8,174 are used to fit performance profiles. Recurrence reproduces the reported explicit-simulation metrics across 35 calibrated-profile configurations, as well as additional workload and cadence validation cases. For a 16-job anchor workload, it preserves 59,743,936 decoding events while achieving a 24.0x host-side speedup, and recurrent simulations scale beyond 1.22 billion events. Across 17 reference configurations, the automatics taged-fluid mode yields a mean makespan error of 2.60% and a worst-case error of 6.45%. Design-space studies reveal transfer-limited resource matching,decoder-driven pipeline stalls, and saturation of dedicated resources under microsecond-scale QEC cycles. Subjects: Quantum Physics (quant-ph); Performance (cs.PF) Cite as: arXiv:2607.13351 [quant-ph] (or arXiv:2607.13351v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.13351 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ying Mao [view email] [v1] Wed, 15 Jul 2026 00:23:18 UTC (1,976 KB) Full-text links: Access Paper: View a PDF of the paper titled StreamingQEC: Streaming Quantum Error Correction in Tightly Integrated Quantum-Classical Systems via Certified Recurrence, by Panayiotis Christou and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-07 Change to browse by: cs cs.PF 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?)