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DTSTART:20240310T070000
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DTSTART:20231105T060000
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DTSTART;TZID=America/Toronto:20240611T100000
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URL:https://uwaterloo.ca/institute-for-quantum-computing/events/classical-v
 erification-quantum-learning
SUMMARY:Classical Verification of Quantum Learning
CLASS:PUBLIC
DESCRIPTION:CS/MATH SEMINAR MARCEL HINSCHE FROM FREIE UNIVERSITÄT BERLIN\n
 \nQuantum-Nano Centre\, 200 University Ave West\, Waterloo\, ON CA N2L 3G1
 \nZOOM ONLY\n\nQuantum data access and quantum processing can make certain
 \nclassically intractable learning tasks feasible. However\, quantum\ncapa
 bilities will only be available to a select few in the near\nfuture. Thus\
 , reliable schemes that allow classical clients to\ndelegate learning to u
 ntrusted quantum servers are required to\nfacilitate widespread access to 
 quantum learning advantages. Building\non a recently introduced framework 
 of interactive proof systems for\nclassical machine learning\, we develop 
 a framework for classical\nverification of quantum learning. We exhibit le
 arning problems that a\nclassical learner cannot efficiently solve on thei
 r own\, but that they\ncan efficiently and reliably solve when interacting
  with an untrusted\nquantum prover. Concretely\, we consider the problems 
 of agnostic\nlearning parities and Fourier-sparse functions with respect t
 o\ndistributions with uniform input marginal. We propose a new quantum\nda
 ta access model that we call \"mixture-of-superpositions\" quantum\nexampl
 es\, based on which we give efficient quantum learning algorithms\nfor the
 se tasks. Moreover\, we prove that agnostic quantum parity and\nFourier-sp
 arse learning can be efficiently verified by a classical\nverifier with on
 ly random example or statistical query access.\nFinally\, we showcase two 
 general scenarios in learning and\nverification in which quantum mixture-o
 f-superpositions examples do\nnot lead to sample complexity improvements o
 ver classical data. Our\nresults demonstrate that the potential power of q
 uantum data for\nlearning tasks\, while not unlimited\, can be utilized by
  classical\nagents through interaction with untrusted quantum entities.
DTSTAMP:20260424T125048Z
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