Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking
Summarize this article with:
Quantum Physics arXiv:2604.06270 (quant-ph) [Submitted on 7 Apr 2026] Title:Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking Authors:Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono, Haza Nuzly Bin Abdull Hamed View a PDF of the paper titled Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking, by Riza Alaudin Syah and 3 other authors View PDF Abstract:Efficient data encoding is the main factor affecting how fast hybrid quantum-classical algorithms run, but traditional simulators spend most of their time changing classical features into quantum rotations. This work introduces Hybriqu Encoder, a Rust-based, SIMD-aware kernel that focuses exclusively on angle encoding and integrates transparently with Python via CFFI. The kernel processes four double-precision rotations at once using AVX-class vector lanes, combines data in a way that fits well with the cache and uses pre-calculated trigonometric factors, while keeping all unsafe operations within a safe Rust interface. Benchmarks on Apple Silicon show that using pure angle encoding is 5.4% faster at 64 qubits, and the speedup increases as the amount of data exceeds the L1 cache size, while kernels that quickly apply rotations to the entire state vector are limited by memory and do not benefit from SIMD. These results indicate that using vectorization leads to consistent improvements when calculations are the main focus, but limits on data transfer speed prevent additional speed increases, highlighting the need for future efforts on better state updates and choosing between different processing methods. By combining smart optimization that considers the architecture with Rust's safety features, the Hybriqu Encoder offers a flexible base for bigger, mixed systems designed to reduce data encoding delays in future hybrid quantum-classical processes. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.06270 [quant-ph] (or arXiv:2604.06270v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.06270 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1109/ICEEIE66203.2025.11252216 Focus to learn more DOI(s) linking to related resources Submission history From: Riza Alaudin Syah [view email] [v1] Tue, 7 Apr 2026 05:47:03 UTC (456 KB) Full-text links: Access Paper: View a PDF of the paper titled Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking, by Riza Alaudin Syah and 3 other authorsView PDF 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?)