BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20270314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261007T024130Z UID:C8694CBF-E01A-4AE1-921C-5C5AE9FC3EAA DTSTART;TZID=America/New_York:20261102T110000 DTEND;TZID=America/New_York:20261102T123000 DESCRIPTION:Reconfigurable intelligent surfaces (RISs) offer a promising ap proach to improving the spectral and energy efficiency of next-generation wireless networks. By deploying programmable surfaces on walls\, windows\, and other objects\, and jointly optimizing them with wireless endpoints\, smart radio environments can dynamically shape propagation to enhance cov erage and connectivity. Realizing this vision\, however\, requires efficie nt optimization over vast configuration spaces and rapid prediction of com plex electromagnetic interactions among RISs\, the environment\, and wirel ess endpoints.\n\nIn this talk\, we will explore how quantum computing and machine intelligence can address these complementary challenges. We will discuss physics-based formulations that map RIS configuration problems to Ising Hamiltonians\, enabling quantum-assisted and hybrid classical–quan tum optimization. We will then introduce Photon Splatting\, a physics-guid ed neural surrogate for real-time\, site-specific channel prediction in co mplex quasi-static environments. Looking ahead\, we will briefly explore a n emerging perception-to-propagation direction that connects sparse visual observations to uncertainty-aware full-wave prediction through probabilis tic scene representations and a stochastic boundary-integral solver. Toget her\, these developments point toward smart radio environments that can be perceived\, predicted\, and programmed\, advancing the vision of intellig ent and adaptive 6G networks.\n\nSpeaker(s): Prof. Zhen Peng\n\nRoom: GB12 0\, Bldg: Galbraith Building\, 35 St George St\, Toronto\, Ontario\, Canad a\, M5S 1A4 LOCATION:Room: GB120\, Bldg: Galbraith Building\, 35 St George St\, Toronto \, Ontario\, Canada\, M5S 1A4 ORGANIZER:costas.sarris@utoronto.ca SEQUENCE:39 SUMMARY:Empowering Smart Radio Environments with Quantum Computing and Mach ine Intelligence (Prof. Zhen Peng\, UIUC) URL;VALUE=URI:https://events.vtools.ieee.org/m/583068 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\; text-justify: inter-ideograph\; line-height: normal\; background: whi tesmoke\; margin: 15.0pt 0cm 15.0pt 0cm\;"><span lang="EN-US" style="font- family: 'Segoe UI'\,sans-serif\; mso-fareast-font-family: 'Times New Roman '\; color: black\;">Reconfigurable intelligent surfaces (RISs) offer a pro mising approach to improving the spectral and energy efficiency of next-ge neration wireless networks. By deploying programmable surfaces on walls\, windows\, and other objects\, and jointly optimizing them with wireless en dpoints\, smart radio environments can dynamically shape propagation to en hance coverage and connectivity. Realizing this vision\, however\, require s efficient optimization over vast configuration spaces and rapid predicti on of complex electromagnetic interactions among RISs\, the environment\, and wireless endpoints.</span></p>\n<p class="MsoNormal" style="text-align : justify\; text-justify: inter-ideograph\; line-height: normal\; backgrou nd: whitesmoke\; margin: 15.0pt 0cm 15.0pt 0cm\;"><span lang="EN-US" style ="font-family: 'Segoe UI'\,sans-serif\; mso-fareast-font-family: 'Times Ne w Roman'\; color: black\;">In this talk\, we will explore how quantum comp uting and machine intelligence can address these complementary challenges. We will discuss physics-based formulations that map RIS configuration pro blems to Ising Hamiltonians\, enabling quantum-assisted and hybrid classic al&ndash\;quantum optimization. We will then introduce Photon Splatting\, a physics-guided neural surrogate for real-time\, site-specific channel pr ediction in complex quasi-static environments. Looking ahead\, we will bri efly explore an emerging perception-to-propagation direction that connects sparse visual observations to uncertainty-aware full-wave prediction thro ugh probabilistic scene representations and a stochastic boundary-integral solver. Together\, these developments point toward smart radio environmen ts that can be perceived\, predicted\, and programmed\, advancing the visi on of intelligent and adaptive 6G networks. </span></p> END:VEVENT END:VCALENDAR