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DESCRIPTION:Optimal Bounds for Quantum Learning via Information Theory\n​\nI will discuss our recent work on finding lower bounds to solve 3 problems in Quantum Learning Theory: Quantum PAC learning\, Quantum Agnostic Learning and Quantum Coupon Collector. Our main goal was to use tools from Quantum Information Theory\, specifically the data processing inequality\, to obtain these results\, instead of going for more exotic ones. We succeed in doing so for the first two problems\, and we show concretely that it doesn't work for the last problem\, due to an inherent loss of information that is possible even for valid learning algorithms\, for which we give a bound using an alternate method that utilizes the analysis we went through previously. We hope that these tools are broadly applicable to other quantum learning problems.
X-ALT-DESC;FMTTYPE=text/html:<strong>Optimal Bounds for Quantum Learning via Information Theory</strong><br />​<br />I will discuss our recent work on finding lower bounds to solve 3 problems in Quantum Learning Theory: Quantum PAC learning, Quantum Agnostic Learning and Quantum Coupon Collector. Our main goal was to use tools from Quantum Information Theory, specifically the data processing inequality, to obtain these results, instead of going for more exotic ones. We succeed in doing so for the first two problems, and we show concretely that it doesn't work for the last problem, due to an inherent loss of information that is possible even for valid learning algorithms, for which we give a bound using an alternate method that utilizes the analysis we went through previously. We hope that these tools are broadly applicable to other quantum learning problems.
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SUMMARY:IQC Student Seminar Featuring Pulkit Sinha
DTSTART;TZID=America/New_York:20230712T120000
DTEND;TZID=America/New_York:20230712T130000
DTSTAMP:20260421T201501Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:QNC 1201
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