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SUMMARY:Hsin Yuan Huang\, (Robert) (Caltech)
DTSTART:20210317T180000Z
DTEND:20210317T190000Z
DTSTAMP:20260423T003254Z
UID:MPML/36
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/36/">In
 formation-theoretic bounds on quantum advantage in machine learning</a>\nb
 y Hsin Yuan Huang\, (Robert) (Caltech) as part of Mathematics\, Physics an
 d Machine Learning (IST\, Lisbon)\n\n\nAbstract\nWe compare the complexity
  of training classical and quantum machine learning (ML) models for predic
 ting outcomes of physical experiments. The experiments depend on an input 
 parameter x and involve the execution of a (possibly unknown) quantum proc
 ess $E$. Our figure of merit is the number of runs of $E$ needed during tr
 aining\, disregarding other measures of complexity. A classical ML perform
 s a measurement and records the classical outcome after each run of $E$\, 
 while a quantum ML can access $E$ coherently to acquire quantum data\; the
  classical or quantum data is then used to predict outcomes of future expe
 riments. We prove that\, for any input distribution $D(x)$\, a classical M
 L can provide accurate predictions on average by accessing $E$ a number of
  times comparable to the optimal quantum ML. In contrast\, for achieving a
 ccurate prediction on all inputs\, we show that exponential quantum advant
 age exists in certain tasks. For example\, to predict expectation values o
 f all Pauli observables in an $n-$qubit system\, we present a quantum ML u
 sing only $O(n)$ data and prove that a classical ML requires $2^{\\Omega(n
 )}$ data.\n
LOCATION:https://researchseminars.org/talk/MPML/36/
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