PyEncode: An Open-Source Library for Structured Quantum State Preparation

[2603.28259] PyEncode: An Open-Source Library for Structured Quantum State Preparation Skip to main content Search Submit Donate Log in Computer Science > Emerging Technologies arXiv:2603.28259 (cs) [Submitted on 30 Mar 2026 ( v1 ), last revised 24 Aug 2026 (this version, v8)] Title: PyEncode: An Open-Source Library for Structured Quantum State Preparation Authors: Krishnan Suresh , Sanjay Suresh View PDF HTML (experimental) Abstract: Quantum algorithms require encoding classical vectors as quantum states, a step known as amplitude encoding. General-purpose routines produce circuits with $\bigO{2^m}$ gates for vectors of length $N = 2^m$. However, vectors arising in scientific and engineering applications often exhibit mathematical structure that admits far more efficient encoding. Theoretical work over the last decade has established efficient circuits for several structured vector classes, but without open-source implementations. We present \textbf{PyEncode}, an open-source Python library that implements this body of theory in a unified framework. It covers ten exact pattern families: \emph{sparse, step, square, Walsh, Fourier, geometric, Hamming, staircase, Dicke}, and \emph{polynomial}. A function \texttt{encode} maps each pattern to a verified Qiskit circuit, with no vector materialization and no approximation; for example, \texttt{encode(SPARSE([(19, 1.0)]), N=64)} encodes the vector $\mathbf{e}_{19}$ of length $N = 64$. Sparse, step, Walsh, Hamming, and staircase patterns require $\bigO{m}$ gates; square and Fourier patterns require $\bigO{m^2}$; Dicke states $|D^m_k\rangle$ require $\bigO{k(m-k)}$; degree-$d$ polynomials require $\bigO{m^{d+1}}$. A companion \texttt{predict\_gates} function estimates transpiled gate counts without synthesis. Three composition primitives are supported: \texttt{SUM} for weighted superpositions, \texttt{PARTITION} for ancilla-free composition of disjoint-support patterns, and \texttt{TENSOR} for separable states over disjoint subregisters. For amplitude vectors outside these exact families, PyEncode also provides a matrix product state (MPS) loader, \texttt{encode\_mps}. The library is available at this https URL . Subjects: Emerging Technologies (cs.ET) Cite as: arXiv:2603.28259 [cs.ET] (or arXiv:2603.28259v8 [cs.ET] for this version) https://doi.org/10.48550/arXiv.2603.28259 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Krishnan Suresh [ view email ] [v1] Mon, 30 Mar 2026 10:37:56 UTC (1,570 KB) [v2] Wed, 1 Apr 2026 11:45:24 UTC (1,581 KB) [v3] Thu, 2 Apr 2026 10:31:05 UTC (1,578 KB) [v4] Sun, 19 Apr 2026 14:03:42 UTC (1,984 KB) [v5] Sat, 25 Apr 2026 14:59:42 UTC (1,683 KB) [v6] Sun, 3 May 2026 23:37:26 UTC (1,853 KB) [v7] Sat, 4 Jul 2026 10:40:32 UTC (2,840 KB) [v8] Mon, 24 Aug 2026 23:23:27 UTC (2,443 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.ET < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations 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 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. 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