Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration Yen Jui Chang, Wei-Ting Wang, Chen-Yu Liu, Yun-Yuan Wang, Ching-Ray Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528572/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Apr, 2026 Read the published version in Quantum Machine Intelligence → Version 1 posted 11 You are reading this latest preprint version Abstract We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states toward desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and the generation of structured two-dimensional patterns, exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. Quantum Computing Split-Step Quantum Walks Entangled Quantum Walks Adaptive Distribution Generation CUDA-Q Variational Quantum Circuits Generative Modeling Quantum State Preparation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Apr, 2026 Read the published version in Quantum Machine Intelligence → Version 1 posted Editorial decision: Revision requested 05 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 27 Apr, 2025 Editor assigned by journal 27 Apr, 2025 Submission checks completed at journal 27 Apr, 2025 First submitted to journal 25 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6528572","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449429880,"identity":"6643924d-948d-435f-857d-0f33f05baa69","order_by":0,"name":"Yen Jui 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