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DTSTART;TZID=America/Toronto:20230726T120000
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URL:https://uwaterloo.ca/institute-for-quantum-computing/events/iqc-student
 -seminar-featuring-evan-peters
LOCATION:QNC - Quantum Nano Centre 200 University Avenue West QNC 1201 Wate
 rloo ON N2L 3G1 Canada
SUMMARY:IQC Student Seminar Featuring Evan Peters
CLASS:PUBLIC
DESCRIPTION:SOME LEARNING BOUNDS AND GUARANTEES FOR TESTING (QUANTUM) HYPOT
 HESES\n\nMachine learning is a powerful tool\, yet we often do not know ho
 w well\na learning algorithm might perform on any given task. One standard
 \napproach to bound the accuracy of a learning algorithm is to reduce\nthe
  learning task to hypothesis testing. Fano's inequality then states\nthat 
 a large amount of mutual information between the learner's\nobservations a
 nd the set of unknown parameters is a necessary\ncondition for success. \n
 \nIn this talk\, I will describe how such a condition is also sufficient\n
 for succeeding at some learning task\, thereby providing a purely\ninforma
 tion-theoretic guarantee for learning. Noting that this\nguarantee has an 
 immediate extension to quantum information theory\, I\nwill then introduce
  the task of \"testing quantum hypotheses\"\, in which\nthe unknown parame
 ters of the learning task are prepared in a quantum\nregister in superposi
 tion (rather than being sampled stochastically)\nand the learner's success
  at this task is measured by their ability to\nestablish quantum correlati
 ons with that register. I will discuss\nongoing attempts to characterize t
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DTSTAMP:20260406T184850Z
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