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10.1038/s41467-017-00705-2 http://scihub22266oqcxt.onion/10.1038/s41467-017-00705-2 C5610197!5610197!28939812     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28939812|t=2017. Efficient representation of quantum many-body states with deep neural networks |pdf=|usr=}}{{28939812}} gab.com Text Efficient representation of quantum many-body states with deep neural networks JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28939812 #FOAvip Part of the challenge for quantum many-body problems comes from the difficulty of representing large-scale quantum states, which in general requires an exponentially large number of parameters. Neural networks provide a powerful tool to represent quantum many-body states. An important open question is what characterizes the representational power of deep and shallow neural networks, which is of fundamental interest due to the popularity of deep learning methods. Here, we give a proof that, assuming a widely believed computational complexity conjecture, a deep neural network can efficiently represent most physical states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics, while a shallow network representation with a restricted Boltzmann machine cannot efficiently represent some of those states. Twit Text FOAVip Efficient representation of quantum many-body states with deep neural networks ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28939812 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28939812 #FOAvip English Wikipedia <ref>{{Cite pmid|28939812}}</ref> Efficient representation of quantum many-body states with deep neural networks #MMPMID28939812 Gao X; Duan LM Nat Commun 2017[]; 8 (ä): ä PMID28939812show ga Part of the challenge for quantum many-body problems comes from the difficulty of representing large-scale quantum states, which in general requires an exponentially large number of parameters. Neural networks provide a powerful tool to represent quantum many-body states. An important open question is what characterizes the representational power of deep and shallow neural networks, which is of fundamental interest due to the popularity of deep learning methods. Here, we give a proof that, assuming a widely believed computational complexity conjecture, a deep neural network can efficiently represent most physical states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics, while a shallow network representation with a restricted Boltzmann machine cannot efficiently represent some of those states. äEfficient representation of quantum many-body states with deep neural (epmc).pdf DeepDyve Pubget Overpricing