pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis

Quantum Physics [Submitted on 23 Apr 2026] pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis Shuntaro Yamamoto, Nobuyuki Yoshioka 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: 31 pages, 8 figures 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 Submission history From: Nobuyuki Yoshioka [view email] [v1] Thu, 23 Apr 2026 06:44:35 UTC (99 KB) Access Paper: view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)