{"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/hybrid-discrete-continuous-compilation-of","title":"Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning","arxiv_id":"2307.05744","date":"2023-07-12","proceeding":null,"authors":["Francesco Preti","Michael Schilling","Sofiene Jerbi","Lea M. Trenkwalder","Hendrik Poulsen Nautrup","Felix Motzoi","Hans J. Briegel"],"abstract":"Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous optimization across a continuous gate set, and architecture-tailored implementation. The continuous parameters are discovered with a gradient-based optimization algorithm, while in tandem the optimal gate orderings are learned via a deep reinforcement learning algorithm, based on projective simulation. To test this approach, we introduce a framework to simulate collective gates in trapped-ion systems efficiently on a classical device. The algorithm proves able to significantly reduce the size of relevant quantum circuits for trapped-ion computing. Furthermore, we show that our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.","url_abs":"https://arxiv.org/abs/2307.05744v3","url_pdf":"https://arxiv.org/pdf/2307.05744v3.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":"hybrid-discrete-continuous-compilation-of","repo_url":"https://github.com/franz3105/rl_ion_gates","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}