Q-PIPE A Practical Quantum Phase Encoding Method
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Quantum Physics arXiv:2604.09869 (quant-ph) [Submitted on 10 Apr 2026] Title:Q-PIPE A Practical Quantum Phase Encoding Method Authors:Brian García Sarmina, Emmanuel Martínez-Guerrero, Janeth De Anda Gil, Sun Guo-Hua, Dong Shi-Hai View a PDF of the paper titled Q-PIPE A Practical Quantum Phase Encoding Method, by Brian Garc\'ia Sarmina and 4 other authors View PDF Abstract:A major hurdle in Quantum Image Processing (QIMP) is efficiently transferring classical, high-dimensional image data into quantum states. Current methods face trade-offs: amplitude encoding (FRQI) is computationally expensive in gate complexity and limited arithmetic capabilities, while basis encoding (NEQR) incurs heavy initialization overhead scaling with image resolution and intensity bit-depth. Frequency-domain approaches further demand complex transformations for basic pixel-wise arithmetic and extensive post-processing to reconstruct pixel information. To address the lack of practical phase encodings, we introduce Q-PIPE (Quantum-Gray Phase Injection for Pixel Encoding). Exploiting the quantum phase kickback mechanism and optimized spatial traversal via a Gray-code sequence, Q-PIPE efficiently maps continuous intensity values into the computational basis with an elementary gate count of $O(qN)$ a $O(\text{log}N)$ improvement over standard basis encoding. Operating directly in the phase domain enables native computation of finite differences without deep arithmetic circuits. Classical readout vulnerabilities, including phase aliasing and spectral leakage, are mitigated by mapping inputs to $[-\pi, \pi]$ and introducing a probability threshold equation that scales inversely with the dimension of the spatial register. A proof-of-concept performing Quantum Edge Detection (QED) via directional derivatives demonstrates strong accuracy, yielding exact reconstructions for quantized inputs and low Mean Absolute Error (MAE) for continuous data across multiple benchmark datasets. Ultimately, Q-PIPE establishes a highly parallelizable, NISQ-compatible subroutine that advances quantum computer vision while reducing input/output (I/O) data-loading overhead in broader Quantum Machine Learning (QML) workflows. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.09869 [quant-ph] (or arXiv:2604.09869v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.09869 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Emmanuel Martínez Guerrero [view email] [v1] Fri, 10 Apr 2026 19:59:25 UTC (4,487 KB) Full-text links: Access Paper: View a PDF of the paper titled Q-PIPE A Practical Quantum Phase Encoding Method, by Brian Garc\'ia Sarmina and 4 other authorsView PDFTeX Source 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?)