{"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/quantum-machine-learning-with-canonical","title":"Quantum Machine Learning With Canonical Variables","arxiv_id":"2406.06666","date":"2024-06-10","proceeding":null,"authors":["Jesús Fuentes"],"abstract":"Utilising dynamic electromagnetic field control over charged particles serves as the basis for a quantum machine learning platform that operates on observables rather than directly on states. Such a platform can be physically realised in ion traps or particle confinement devices that utilise electromagnetic fields as the source of control. The electromagnetic field acts as the ansatz within the learning algorithm. The models discussed are exactly solvable, with exact solutions serving as precursors for learning tasks to emerge, including regression and classification algorithms as particular cases. This approach is considered in terms of canonical variables with semi-classical behaviour, disregarding relativistic degrees of freedom.","url_abs":"https://arxiv.org/abs/2406.06666v1","url_pdf":"https://arxiv.org/pdf/2406.06666v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"quantum-machine-learning-with-canonical","repo_url":"https://github.com/fuentesigma/ionlearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}