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SUMMARY:A quantum computing algorithm to speed up Metropolis sampling - Pr
 of. Guglielmo Mazzola\, Institute for Computational Science\, University o
 f Zurich
DTSTART:20230515T130000Z
DTEND:20230515T133000Z
UID:TALK201007@talks.cam.ac.uk
CONTACT:Dr Venkat Kapil
DESCRIPTION:The task of sampling from a multidimensional finite-temperatur
 e classical Boltzmann probability distribution is a central problem in num
 erical simulations of physics\, chemistry\, and beyond the traditional bou
 ndaries of natural sciences. In this talk\, I will introduce a recent algo
 rithm that can be executed on quantum computers\, offering a scaling advan
 tage compared to state-of-the-art Metropolis schemes. In practice\, we can
  leverage the fact that the collapses of a wave function are uncorrelated 
 and use them as trial updates to obtain non-local but effective moves in t
 he configuration space. The algorithm was invented in 2021 for continuous 
 systems\, where a rigorous justification can be found.[1] Subsequently\, i
 t was adapted to spin systems amenable to hardware implementation\, where 
 it has been experimentally demonstrated.[2]\n\n[1] Mazzola\, PRA\, 104\, 0
 22431 (2021)\n[2] Layden\, Mazzola et al\,  arXiv:2203.12497 (2022)
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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