Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking

Quantum Physics [Submitted on 7 Apr 2026] Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono, Haza Nuzly Bin Abdull Hamed 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: Published in: 2025 9th International Conference On Electrical, Electronics And Information Engineering (ICEEIE) 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 Related DOI: https://doi.org/10.1109/ICEEIE66203.2025.11252216 Focus to learn more Submission history From: Riza Alaudin Syah [view email] [v1] Tue, 7 Apr 2026 05:47:03 UTC (456 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?)