{"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/identifying-quantum-phase-transitions-with","title":"Identifying Quantum Phase Transitions with Adversarial Neural Networks","arxiv_id":"1710.08382","date":"2017-10-11","proceeding":null,"authors":["Patrick Huembeli","Alexandre Dauphin","Peter Wittek"],"abstract":"The identification of phases of matter is a challenging task, especially in\nquantum mechanics, where the complexity of the ground state appears to grow\nexponentially with the size of the system. We address this problem with\nstate-of-the-art deep learning techniques: adversarial domain adaptation. We\nderive the phase diagram of the whole parameter space starting from a fixed and\nknown subspace using unsupervised learning. The input data set contains both\nlabeled and unlabeled data instances. The first kind is a system that admits an\naccurate analytical or numerical solution, and one can recover its phase\ndiagram. The second type is the physical system with an unknown phase diagram.\nAdversarial domain adaptation uses both types of data to create invariant\nfeature extracting layers in a deep learning architecture. Once these layers\nare trained, we can attach an unsupervised learner to the network to find phase\ntransitions. We show the success of this technique by applying it on several\nparadigmatic models: the Ising model with different temperatures, the\nBose-Hubbard model, and the SSH model with disorder. The input is the ground\nstate without any manual feature engineering, and the dimension of the\nparameter space is unrestricted. The method finds unknown transitions\nsuccessfully and predicts transition points in close agreement with standard\nmethods. This study opens the door to the classification of physical systems\nwhere the phases boundaries are complex such as the many-body localization\nproblem or the Bose glass phase.","url_abs":"http://arxiv.org/abs/1710.08382v2","url_pdf":"http://arxiv.org/pdf/1710.08382v2.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":"identifying-quantum-phase-transitions-with","repo_url":"https://github.com/PatrickHuembeli/Adversarial-Domain-Adaptation-for-Identifying-Phase-Transitions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.08382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.08382"}},"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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