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10.1371/journal.pone.0200455 http://scihub22266oqcxt.onion/10.1371/journal.pone.0200455 C6053154!6053154!30024914     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2018 ; 13 (7): ä Nephropedia Template TP {{tp|p=30024914|t=2018. Multiqubit and multilevel quantum reinforcement learning with quantum technologies |pdf=|usr=}}{{30024914}} gab.com Text Multiqubit and multilevel quantum reinforcement learning with quantum technologies JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30024914 #FOAvip We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations. Twit Text FOAVip Multiqubit and multilevel quantum reinforcement learning with quantum technologies ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30024914 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30024914 #FOAvip English Wikipedia <ref>{{Cite pmid|30024914}}</ref> Multiqubit and multilevel quantum reinforcement learning with quantum technologies #MMPMID30024914 Cárdenas-López FA; Lamata L; Retamal JC; Solano E PLoS One 2018[]; 13 (7): ä PMID30024914show ga We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations. äMultiqubit and multilevel quantum reinforcement learning with quantum (epmc).pdf DeepDyve Pubget Overpricing