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10.1038/ncomms10138 http://scihub22266oqcxt.onion/10.1038/ncomms10138 C4737711!4737711!26806491     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2016 ; 7 (ä): ä Nephropedia Template TP {{tp|p=26806491|t=2016. Quantum algorithms for topological and geometric analysis of data |pdf=|usr=}}{{26806491}} gab.com Text Quantum algorithms for topological and geometric analysis of data JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=26806491 #FOAvip Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers?the numbers of connected components, holes and voids?in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis. Twit Text FOAVip Quantum algorithms for topological and geometric analysis of data ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=26806491 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26806491 #FOAvip English Wikipedia <ref>{{Cite pmid|26806491}}</ref> Quantum algorithms for topological and geometric analysis of data #MMPMID26806491 Lloyd S; Garnerone S; Zanardi P Nat Commun 2016[]; 7 (ä): ä PMID26806491show ga Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers?the numbers of connected components, holes and voids?in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis. äQuantum algorithms for topological and geometric analysis of data (epmc).pdf DeepDyve Pubget Overpricing