{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-by-two-dimensional","title":"Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure","arxiv_id":"1710.04833","date":"2017-10-13","proceeding":"ICLR 2018 1","authors":["Ding Liu","Shi-Ju Ran","Peter Wittek","Cheng Peng","Raul Blázquez García","Gang Su","Maciej Lewenstein"],"abstract":"The resemblance between the methods used in quantum-many body physics and in\nmachine learning has drawn considerable attention. In particular, tensor\nnetworks (TNs) and deep learning architectures bear striking similarities to\nthe extent that TNs can be used for machine learning. Previous results used\none-dimensional TNs in image recognition, showing limited scalability and\nflexibilities. In this work, we train two-dimensional hierarchical TNs to solve\nimage recognition problems, using a training algorithm derived from the\nmulti-scale entanglement renormalization ansatz. This approach introduces\nmathematical connections among quantum many-body physics, quantum information\ntheory, and machine learning. While keeping the TN unitary in the training\nphase, TN states are defined, which encode classes of images into quantum\nmany-body states. We study the quantum features of the TN states, including\nquantum entanglement and fidelity. We find these quantities could be properties\nthat characterize the image classes, as well as the machine learning tasks.","url_abs":"http://arxiv.org/abs/1710.04833v4","url_pdf":"http://arxiv.org/pdf/1710.04833v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-by-two-dimensional","repo_url":"https://github.com/dingliu0305/Tree-Tensor-Networks-in-Machine-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"machine-learning-by-two-dimensional","repo_url":"https://github.com/RaulBz/Master_Thesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"machine-learning-by-two-dimensional","repo_url":"https://github.com/RaulBz/Master_Thesis_Code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"tensor-networks","task_name":"Tensor Networks"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.04833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04833"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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