{"created":"2023-06-26T11:01:37.582906+00:00","id":2372,"links":{},"metadata":{"_buckets":{"deposit":"e1c32e22-26ea-4692-a079-51d999a9b5a8"},"_deposit":{"created_by":31,"id":"2372","owners":[31],"pid":{"revision_id":0,"type":"depid","value":"2372"},"status":"published"},"_oai":{"id":"oai:oist.repo.nii.ac.jp:00002372","sets":["6:211"]},"author_link":["15787","15788","15786","15784","15785"],"item_10001_biblio_info_7":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-11-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"19","bibliographicPageStart":"190403","bibliographicVolumeNumber":"127","bibliographic_titles":[{},{"bibliographic_title":"Physical Review Letters","bibliographic_titleLang":"en"}]}]},"item_10001_creator_3":{"attribute_name":"Author","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Borah, Sangkha"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sarma, Bijita"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kewming, Michael"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Milburn, Gerard J."}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Twamley, Jason"}],"nameIdentifiers":[{}]}]},"item_10001_description_5":{"attribute_name":"Abstract","attribute_value_mlt":[{"subitem_description":"Closed loop quantum control uses measurement to control the dynamics of a quantum system to achieve either a desired target state or target dynamics. In the case when the quantum Hamiltonian is quadratic in x and p, there are known optimal control techniques to drive the dynamics toward particular states, e.g., the ground state. However, for nonlinear Hamiltonian such control techniques often fail. We apply deep reinforcement learning (DRL), where an artificial neural agent explores and learns to control the quantum evolution of a highly nonlinear system (double well), driving the system toward the ground state with high fidelity. We consider a DRL strategy which is particularly motivated by experiment where the quantum system is continuously but weakly measured. This measurement is then fed back to the neural agent and used for training. We show that the DRL can effectively learn counterintuitive strategies to cool the system to a nearly pure “cat” state, which has a high overlap fidelity with the true ground state.","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_publisher":"American Physical Society"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:doi/10.1103/PhysRevLett.127.190403","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"Related site","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.190403","subitem_relation_type_select":"URI"}}]},"item_10001_rights_15":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_rights":"© 2021 American Physical Society"}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0031-9007","subitem_source_identifier_type":"ISSN"},{"subitem_source_identifier":"1079-7114","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"Author's flag","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-11-18"}],"displaytype":"detail","filename":"Borah-2021-Measurement-Based Feedback Quantum_1.pdf","filesize":[{"value":"475.3 kB"}],"format":"application/pdf","license_note":"CC BY 4.0\nCreative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"Borah-2021-Measurement-Based Feedback Quantum_1","url":"https://oist.repo.nii.ac.jp/record/2372/files/Borah-2021-Measurement-Based Feedback Quantum_1.pdf"},"version_id":"dbd2e322-237e-4d87-b753-82bea2f7d09e"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"31","path":["211"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-11-18"},"publish_date":"2021-11-18","publish_status":"0","recid":"2372","relation_version_is_last":true,"title":["Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential"],"weko_creator_id":"31","weko_shared_id":31},"updated":"2023-06-26T11:31:49.756889+00:00"}