BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240909T123758Z UID:098ACACB-20E4-446D-8E26-5D6BFB9A0EC5 DTSTART;TZID=America/New_York:20240905T130000 DTEND;TZID=America/New_York:20240905T143000 DESCRIPTION:Abstract: Can we exploit the tools of machine learning for tack ling the complexity of building better quantum computers? In this talk\, I will describe how one can use machine-learning techniques\, specifically reinforcement learning\, to address challenges in this domain. One very im portant area is quantum error correction\, which is needed to fight agains t the unavoidable noise that would otherwise quickly render quantum comput ers useless. The complex feedback strategies needed in this domain can be discovered using model-free or model-based reinforcement learning. Besides discussing our theoretical advances in this area\, I will also describe o ur recent collaboration with experimentalists in learning real-time feedba ck strategies for superconducting qubits.\n\nCo-sponsored by: Montréal Qu antum Photonics Seminar Series\n\nSpeaker(s): Florian Marquardt\n\n6666 Ru e Saint-Urbain\, Montréal\, Quebec\, Canada LOCATION:6666 Rue Saint-Urbain\, Montréal\, Quebec\, Canada ORGANIZER:Benjamin.crockett@ieee.org SEQUENCE:57 SUMMARY:Building better quantum computers using machine learning URL;VALUE=URI:https://events.vtools.ieee.org/m/431396 X-ALT-DESC:Description: <br /><p class="MsoNormal"><img src="https://events .vtools.ieee.org/vtools_ui/media/display/92d5bba8-a75a-4b1f-93fc-b0fa4255b 993" width="991" height="560"></p>\n<p class="MsoNormal"><span lang="EN-US " style="mso-ansi-language: EN-US\;"><strong>Abstract: </strong>Can we exp loit the tools of machine learning for tackling the complexity of building better quantum computers? In this talk\, I will describe how one can use machine-learning techniques\, specifically reinforcement learning\, to add ress challenges in this domain. One very important area is quantum error c orrection\, which is needed to fight against the unavoidable noise that wo uld otherwise quickly render quantum computers useless. The complex feedba ck strategies needed in this domain can be discovered using model-free or model-based reinforcement learning. Besides discussing our theoretical adv ances in this area\, I will also describe our recent collaboration with ex perimentalists in learning real-time feedback strategies for superconducti ng qubits.</span></p> END:VEVENT END:VCALENDAR