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SUMMARY:Grover's algorithm\, databases and quantum machine learning - Aram
  Harrow (MIT)
DTSTART:20190517T110000Z
DTEND:20190517T120000Z
UID:TALK122134@talks.cam.ac.uk
CONTACT:Johannes Bausch
DESCRIPTION:A cornerstone of quantum computing is Grover's 1996 paper: "A 
 Fast Quantum Mechanical Algorithm for Database Search".  Since then\, Grov
 er's algorithm and its descendants have been applied to a wide range of ta
 sks but none have involved databases. In this talk\, I will describe two w
 ays in which Grover search can be used for tasks involving large classical
  databases.  First\, I will describe an example of a task (from high-energ
 y physics) in which the data access costs overheads do not increase the as
 ymptotic run-time.  Second\, I'll show how data reduction techniques can b
 e used to reduce the size of the database\, improving quantum speedups for
  clustering and other tasks in optimization and machine learning. While th
 is talk is mostly focused on Grover\, many of the same points about input 
 data model apply more generally to the adiabatic algorithm\, variational a
 lgorithms\, and other quantum optimization algorithms.
LOCATION:MR14\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge
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