High performance Boson Sampling simulation via data-flow engines

[2309.07027] High performance Boson Sampling simulation via data-flow engines Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2309.07027 (quant-ph) [Submitted on 13 Sep 2023 ( v1 ), last revised 17 Sep 2023 (this version, v2)] Title: High performance Boson Sampling simulation via data-flow engines Authors: Gregory Morse , Tomasz Rybotycki , Ágoston Kaposi , Zoltán Kolarovszki , Uroš Stojčić , Tamás Kozsik , Oskar Mencer , Michał Oszmaniec , Zoltán Zimborás , Péter Rakyta View a PDF of the paper titled High performance Boson Sampling simulation via data-flow engines, by Gregory Morse and 9 other authors View PDF HTML (experimental) Abstract: In this work, we generalize the Balasubramanian-Bax-Franklin-Glynn (BB/FG) permanent formula to account for row multiplicities during the permanent evaluation and reduce the complexity of permanent evaluation in scenarios where such multiplicities occur. This is achieved by incorporating n-ary Gray code ordering of the addends during the evaluation. We implemented the designed algorithm on FPGA-based data-flow engines and utilized the developed accessory to speed up boson sampling simulations up to $40$ photons, by drawing samples from a $60$ mode interferometer at an averaged rate of $\sim80$ seconds per sample utilizing $4$ FPGA chips. We also show that the performance of our BS simulator is in line with the theoretical estimation of Clifford \& Clifford \cite{clifford2020faster} providing a way to define a single parameter to characterize the performance of the BS simulator in a portable way. The developed design can be used to simulate both ideal and lossy boson sampling experiments. Comments: 25 pages Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2309.07027 [quant-ph] (or arXiv:2309.07027v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2309.07027 Focus to learn more arXiv-issued DOI via DataCite Journal reference: New J. Phys. 26 033033 (2024) Related DOI : https://doi.org/10.1088/1367-2630/ad313b Focus to learn more DOI(s) linking to related resources Submission history From: Peter Rakyta [ view email ] [v1] Wed, 13 Sep 2023 15:32:07 UTC (2,639 KB) [v2] Sun, 17 Sep 2023 12:40:59 UTC (2,639 KB) Full-text links: Access Paper: View a PDF of the paper titled High performance Boson Sampling simulation via data-flow engines, by Gregory Morse and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2023-09 References & Citations INSPIRE HEP NASA ADS Google 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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