{"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/free-energy-based-reinforcement-learning","title":"Free energy-based reinforcement learning using a quantum processor","arxiv_id":"1706.00074","date":"2017-05-29","proceeding":null,"authors":["Anna Levit","Daniel Crawford","Navid Ghadermarzy","Jaspreet S. Oberoi","Ehsan Zahedinejad","Pooya Ronagh"],"abstract":"Recent theoretical and experimental results suggest the possibility of using\ncurrent and near-future quantum hardware in challenging sampling tasks. In this\npaper, we introduce free energy-based reinforcement learning (FERL) as an\napplication of quantum hardware. We propose a method for processing a quantum\nannealer's measured qubit spin configurations in approximating the free energy\nof a quantum Boltzmann machine (QBM). We then apply this method to perform\nreinforcement learning on the grid-world problem using the D-Wave 2000Q quantum\nannealer. The experimental results show that our technique is a promising\nmethod for harnessing the power of quantum sampling in reinforcement learning\ntasks.","url_abs":"http://arxiv.org/abs/1706.00074v1","url_pdf":"http://arxiv.org/pdf/1706.00074v1.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":"free-energy-based-reinforcement-learning","repo_url":"https://github.com/Mircea-Marian/attract_grid_data_flow_optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}