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10.1098/rspa.2015.0301 http://scihub22266oqcxt.onion/10.1098/rspa.2015.0301 C4614442!4614442 !26528079     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26528079 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Math+Phys+Eng+Sci 2015 ; 471 (2181 ): 20150301 Nephropedia Template TP {{tp|p=26528079 |t=2015. Quantum speedup of Monte Carlo methods |pdf=|usr=}}{{26528079 }} gab.com Text Quantum speedup of Monte Carlo methods JO: Proc Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=26528079 #FOAvip Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the use of quantum walks gives a quantum speedup of the fastest known classical algorithms with rigorous performance bounds for computing partition functions, which use multiple-stage Markov chain Monte Carlo techniques. The quantum algorithm can also be used to estimate the total variation distance between probability distributions efficiently. Twit Text FOAVip Quantum speedup of Monte Carlo methods ????Proc Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=26528079 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26528079 #FOAvip English Wikipedia <ref>{{Cite pmid|26528079 }}</ref> Quantum speedup of Monte Carlo methods #MMPMID26528079 Montanaro A Proc Math Phys Eng Sci 2015[Sep]; 471 (2181 ): 20150301 PMID26528079 show ga Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the use of quantum walks gives a quantum speedup of the fastest known classical algorithms with rigorous performance bounds for computing partition functions, which use multiple-stage Markov chain Monte Carlo techniques. The quantum algorithm can also be used to estimate the total variation distance between probability distributions efficiently. äQuantum speedup of Monte Carlo methods (epmc).pdf DeepDyve Pubget Overpricing