{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T09:56:22Z","timestamp":1747734982450,"version":"3.37.3"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,4]]},"DOI":"10.23919\/ascc56756.2022.9828135","type":"proceedings-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T19:38:23Z","timestamp":1658345903000},"page":"2367-2372","source":"Crossref","is-referenced-by-count":1,"title":["Deep Reinforcement Learning for Control Design of Quantum Gates"],"prefix":"10.23919","author":[{"given":"Shouliang","family":"Hu","sequence":"first","affiliation":[{"name":"Nanjing University,Department of Control and Systems Engineering,Nanjing,China,210093"}]},{"given":"Chunlin","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University,Department of Control and Systems Engineering,Nanjing,China,210093"}]},{"given":"Daoyi","family":"Dong","sequence":"additional","affiliation":[{"name":"University of New South Wales,School of Engineering and Information Technology,Canberra,Australia,ACT 2600"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-44184-5_100161"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2004.11.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.99.042327"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/srep36090"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.68.1500"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2921424"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.90.032310"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2015.359"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ASCC.2015.7244533"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11768-017-7069-y"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2020.3018500"},{"volume-title":"Reinforcement learning: An introduction.","year":"2018","author":"Sutton","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2008.926603"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3153502"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SMC42975.2020.9282921"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2018.8406503"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.98.224305"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLC51923.2020.9469044"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s42484-020-00016-8"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.127.190403"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.126.060401"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.22331\/q-2022-06-28-747"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2283574"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1209\/0295-5075\/126\/60002"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-019-0141-3"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1070\/RM9835"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-019-0201-8"},{"key":"ref28","first-page":"1587","article-title":"Addressing function approximation error in actor-critic methods","volume-title":"International Conference on Machine Learning","author":"Fujimoto"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.32657\/10356\/90191"}],"event":{"name":"2022 13th Asian Control Conference (ASCC)","start":{"date-parts":[[2022,5,4]]},"location":"Jeju, Korea, Republic of","end":{"date-parts":[[2022,5,7]]}},"container-title":["2022 13th Asian Control Conference (ASCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9828005\/9828007\/09828135.pdf?arnumber=9828135","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:55:51Z","timestamp":1706763351000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9828135\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":29,"URL":"https:\/\/doi.org\/10.23919\/ascc56756.2022.9828135","relation":{},"subject":[],"published":{"date-parts":[[2022,5,4]]}}}