pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis
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Quantum Physics arXiv:2604.21333 (quant-ph) [Submitted on 23 Apr 2026] Title:pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis Authors:Shuntaro Yamamoto, Nobuyuki Yoshioka View a PDF of the paper titled pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis, by Shuntaro Yamamoto and Nobuyuki Yoshioka View PDF Abstract:We present pygridsynth, an open-source Python library for ancilla-free approximate Clifford+$T$ synthesis that runs in $O(\log(1/\epsilon))$ for precision $\epsilon$. For $n=1, 2$ qubits, the library builds upon established efficient and high-precision synthesis routines, such as nearly optimal $Z$-rotation synthesis and magnitude approximation. For $n\ge 3$ qubits, we introduce a partial-decomposition technique that generalizes the magnitude approximation, reducing constant factors in the $T$-count as $(\frac{21}{8}\cdot 4^n - \frac{9}{2}\cdot 2^n + 9)\log_2(1/\epsilon) + o(\log(1/\epsilon))$. The package also exposes a mixed-synthesis workflow that approximates target unitary channels by probabilistic mixtures of Clifford+$T$ circuits, for which we empirically find that the synthesis error is reduced from $\epsilon$ to $\epsilon^2/(2n)$. Taken together, these features make pygridsynth a Python-native platform for high-precision Clifford$+T$ synthesis and for benchmarking unitary and mixed synthesis strategies on multi-qubit instances. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.21333 [quant-ph] (or arXiv:2604.21333v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.21333 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nobuyuki Yoshioka [view email] [v1] Thu, 23 Apr 2026 06:44:35 UTC (99 KB) Full-text links: Access Paper: View a PDF of the paper titled pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis, by Shuntaro Yamamoto and Nobuyuki YoshiokaView PDFTeX 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?)