ShadowNet for Data-Centric Quantum System Learning

[2308.11290] ShadowNet for Data-Centric Quantum System Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2308.11290 (quant-ph) [Submitted on 22 Aug 2023 ( v1 ), last revised 16 Aug 2026 (this version, v2)] Title: ShadowNet for Data-Centric Quantum System Learning Authors: Yuxuan Du , Yibo Yang , Tongliang Liu , Zhouchen Lin , Bernard Ghanem , Dacheng Tao View a PDF of the paper titled ShadowNet for Data-Centric Quantum System Learning, by Yuxuan Du and 5 other authors View PDF HTML (experimental) Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network protocols and classical shadows, while both methods have limitations: the former suffers from incompatible dataset construction rules, resulting in substantial computational demands for data collection when addressing different tasks; the latter lacks the ability to distill knowledge from prior data to enhance subsequent learning endeavors. In this study, we propose a data-centric learning paradigm combining the strengths of these two approaches to advance quantum system learning (QSL). Central to our paradigm lies a unified dataset construction rule, achieved by classical shadows along with other easily obtainable information of quantum systems. To illustrate our approach, we present ShadowNet, implemented under both convolutional and attention mechanisms, to efficiently and faithfully tackle two pivotal QSL tasks: quantum state tomography (QST) and direct fidelity estimation (DFE). Numerical simulations on QST and DFE up to 60 qubits validate the efficacy of our proposal, showcasing how ShadowNet advances classical shadows with limited state copies, and highlighting how the varied neural networks impact the performance. Our work underscores the immense potential of a data-centric approach in comprehending novel and large quantum systems. Comments: Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 pages. 12 Figures Subjects: Quantum Physics (quant-ph) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2308.11290 [quant-ph] (or arXiv:2308.11290v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2308.11290 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuxuan Du [ view email ] [v1] Tue, 22 Aug 2023 09:11:53 UTC (771 KB) [v2] Sun, 16 Aug 2026 03:02:43 UTC (787 KB) Full-text links: Access Paper: View a PDF of the paper titled ShadowNet for Data-Centric Quantum System Learning, by Yuxuan Du and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2023-08 Change to browse by: cs cs.AI cs.LG 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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