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:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20211107T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:6836cfa18461b DTSTART;TZID=America/Toronto:20221102T120000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20221102T130000 URL:/institute-for-quantum-computing/events/iqc-student -seminar-featuring-jose-polo-gomez SUMMARY:IQC Student Seminar featuring Jose Polo Gomez CLASS:PUBLIC DESCRIPTION:Summary \n\nMEASURING QUANTUM FIELDS WITH PARTICLE DETECTORS AN D MACHINE LEARNING\n\nABSTRACT: The model for measurements used in quantu m mechanics (based\non the projection postulate) cannot be extended to mod el measurements\nof quantum fields\, since they are incompatible with rela tivity. We\nwill see that measurements performed with particle detectors ( i.e.\,\nlocalized non-relativistic quantum systems that couple covariantly to\nquantum fields) are consistent with relativity\, and that they allow us\nto build a consistent measurement theory for QFT. For this measurement \nframework to be of practical use\, we need to understand how can we\nmea sure specific properties of the field using a particle detector. I\nwill s how that there is a simple fixed measurement protocol that\nallows us to e xtract essentially all the information about the field\nthat the detector gathers\, and that this information can then be\ninterpreted to study a sp ecific targeted feature using machine\nlearning techniques. Specifically\, I will examine two examples in\nwhich we use a neural network to extract global information about the\nfield (boundary conditions and temperature) performing local\nmeasurements\, taking advantage of the fact that this gl obal\ninformation is stored locally by the field\, albeit in a scrambled w ay.\n DTSTAMP:20250528T085601Z END:VEVENT END:VCALENDAR