BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:68cd9b8c37f0a
DTSTART;TZID=America/Toronto:20240416T150000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20240416T160000
URL:/institute-for-quantum-computing/events/recent-prog
 ress-hamiltonian-learning
SUMMARY:Recent progress in Hamiltonian learning
CLASS:PUBLIC
DESCRIPTION:Summary \n\nCS/MATH SEMINAR - YU TONG\, CALTECH\n\nQuantum-Nano
  Centre\, 200 University Ave West\, Room QNC 1201 + ZOOM\nÀ¶Ý®ÊÓƵ\, ON CA
  N2L 3G1\n\nIn the last few years a number of works have proposed and impr
 oved\nprovably efficient algorithms for learning the Hamiltonian from\nrea
 l-time dynamics. In this talk\, I will first provide an overview of\nthese
  developments\, and then discuss how the Heisenberg limit\, the\nfundament
 al precision limit imposed by quantum mechanics\, can be\nreached for this
  task. I will demonstrate how the Heisenberg limit\nrequires techniques th
 at are fundamentally different from previous\nones\, and the important rol
 es played by quantum control and\nthermalization. I will also discuss open
  problems that are crucial to\nmaking these algorithms implementable on cu
 rrent devices.\n 
DTSTAMP:20250919T180604Z
END:VEVENT
END:VCALENDAR