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10.1038/srep45672 http://scihub22266oqcxt.onion/10.1038/srep45672 C5395824!5395824!28422093     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       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=28422093|t=2017. Quantum Enhanced Inference in Markov Logic Networks |pdf=|usr=}}{{28422093}} gab.com Text Quantum Enhanced Inference in Markov Logic Networks JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28422093 #FOAvip Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning. Twit Text FOAVip Quantum Enhanced Inference in Markov Logic Networks ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28422093 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28422093 #FOAvip English Wikipedia <ref>{{Cite pmid|28422093}}</ref> Quantum Enhanced Inference in Markov Logic Networks #MMPMID28422093 Wittek P; Gogolin C Sci Rep 2017[]; 7 (ä): ä PMID28422093show ga Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning. äQuantum Enhanced Inference in Markov Logic Networks (epmc).pdf DeepDyve Pubget Overpricing