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UID:6a2ec3ca3db74
DTSTART:20250324T110000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20250324T120000Z
LOCATION:Seminar Room
SUMMARY:ICFO | MIGUEL NAVASCUES
CLASS:PUBLIC
DESCRIPTION:In this talk\, I will consider the problem of predicting future
  averages of an unknown quantum observable\, given a dataset of noisy past
  values. The observable\, the initial state of the physical system and eve
 n the nature of the latter are unknown. There is\, however\, a promise on 
 the energy distribution of the state: with very high probability\, it is c
 onstrained to be smaller than a threshold. In this mostly unexplored frame
 work\, one can find very funny objects\, like self-testing datasets\, whic
 h can only be generated with specific Hamiltonians\, states and measuremen
 t operators\; or &ldquo\;aha!&rdquo\; datasets\, where predictability cons
 iderably increases when we add datapoints from an unrelated measurement. M
 ore weirdly\, some simple datasets lead to what we call a &ldquo\;fog bank
 &rdquo\;: complete unpredictability at some extrapolation time \\tau\, and
  exact predictability for time \\tau&rsquo\;&gt\;\\tau. At the end of the 
 talk\, I will present some general no-go results on extrapolation and prov
 ide a hierarchy of efficient SDP relaxations of the set of feasible datapo
 ints\, with bounds on the speed of convergence.
DTSTAMP:20260614T150754Z
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