BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250928T104240Z UID:15E0B329-90E0-4094-80DF-4DAA6A9ACF50 DTSTART;TZID=America/New_York:20250926T120000 DTEND;TZID=America/New_York:20250926T130000 DESCRIPTION:Join us for the exciting webinar series on "New Frontiers in Si gnal Processing in 6G Wireless Networks"\, a collaboration between IEEE Si gnal Processing\, IEEE Communications Society chapters in Ottawa\, and IEE E ComSoC Young Professionals. Kicking off on September 26th\, this series features leading professors exploring the latest advancements in 6G techno logy. Don’t miss the first session!\n\nRegister now and stay tuned for u pdates on upcoming speakers and topics!\n\nSpeaker(s): Prof. Walid Saad\n\ nAgenda: \nQuantum technologies are poised to transform next-generation co mmunication and artificial intelligence (AI) systems. This talk explores t wo key directions: (a) scaling quantum communication networks (QCNs) and ( b) designing quantum-native reinforcement learning algorithms. Scaling QCN s across nodes and geographies remains a critical challenge for realizing a quantum Internet (QI) that integrates quantum security\, computing\, and machine learning. We examine quantum repeater networks (QRNs)\, that form the backbone for large-scale QCNs\, with a focus on how to scale repeater counts and spacing to manage probabilistic entanglement operations while maintaining quality-of-service. We then investigate free-space optical (FS O) quantum channels\, where reflective intelligent surfaces (RISs) can mit igate environmental obstacles in settings lacking conventional infrastruct ure. Beyond connectivity\, enabling effective collaboration among distribu ted artificial intelligence (AI) agents emerges as a second major challeng e—one where quantum technologies can offer transformative advantages. Ex isting multi-agent reinforcement learning (MARL) methods\, including recen t quantum MARL frameworks\, typically rely on classical information exchan ge\, limiting scalability and efficiency. To overcome this\, we introduce entangled quantum multi-agent reinforcement learning (eQMARL): a quantum-n ative AI framework where quantum entanglement forms the core mechanism for coordination. In eQMARL\, a quantum-entangled split critic links local ob servation encoders via entangled qubits\, removing the need for explicit d ata sharing and reducing classical communication overhead. Joint quantum m easurements enable coordinated policy updates with far fewer centralized p arameters. Experiments show that eQMARL improves convergence tiem and achi eves higher scores than classical and quantum baselines\, and reduces cent ralized parameters significantly compared to the split classical baseline. We conclude the talk with an overview on important open problems in these areas\, as well as some of our ongoing research activities.\n\nVirtual: h ttps://events.vtools.ieee.org/m/499855 LOCATION:Virtual: https://events.vtools.ieee.org/m/499855 ORGANIZER:monireh.vamegh@ieee.org SEQUENCE:31 SUMMARY:From Quantum Connectivity to Quantum-Native Artificial Intelligence URL;VALUE=URI:https://events.vtools.ieee.org/m/499855 X-ALT-DESC:Description: <br /><p style="margin: 5.25pt 0in .0001pt 0in\;">< em><span style="font-size: 13.5pt\; color: black\;">Join us for the&nbsp\; exciting&nbsp\;webinar series on&nbsp\;"New Frontiers in Signal Processing in 6G Wireless Networks"\, a collaboration between IEEE Signal Processing \,&nbsp\;IEEE Communications Society&nbsp\;chapters&nbsp\;in Ottawa\, and IEEE ComSoC Young Professionals. Kicking off&nbsp\; on September&nbsp\;26t h\, this series features leading professors exploring the latest advanceme nts in 6G technology. Don&rsquo\;t miss <strong>the first session</strong> !</span></em></p>\n<p style="margin: 5.25pt 0in .0001pt 0in\;"><em><span s tyle="font-size: 13.5pt\; color: black\;">Register now and stay tuned for updates on upcoming speakers and topics!</span></em></p>\n<p style="margin : 5.25pt 0in .0001pt 0in\;">&nbsp\;</p>\n<p><img src="https://events.vtool s.ieee.org/vtools_ui/media/display/4d0872b3-478e-4003-b336-f8d721932d78">< /p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>&n bsp\;</p><br /><br />Agenda: <br /><p>Quantum technologies are poised to t ransform next-generation communication and artificial intelligence (AI) sy stems. This talk explores two key directions: (a) scaling quantum communic ation networks (QCNs) and (b) designing quantum-native reinforcement learn ing algorithms. Scaling QCNs across nodes and geographies remains a critic al challenge for realizing a quantum Internet (QI) that integrates quantum security\, computing\, and machine learning. We examine quantum repeater networks (QRNs)\, that form the backbone for large-scale QCNs\, with a foc us on how to scale repeater counts and spacing to manage probabilistic ent anglement operations while maintaining quality-of-service. We then investi gate free-space optical (FSO) quantum channels\, where reflective intellig ent surfaces (RISs) can mitigate environmental obstacles in settings lacki ng conventional infrastructure. Beyond connectivity\, enabling effective c ollaboration among distributed artificial intelligence (AI) agents emerges as a second major challenge&mdash\;one where quantum technologies can off er transformative advantages. Existing multi-agent reinforcement learning (MARL) methods\, including recent quantum MARL frameworks\, typically rely on classical information exchange\, limiting scalability and efficiency. To overcome this\, we introduce entangled quantum multi-agent reinforcemen t learning (eQMARL):<strong>&nbsp\;</strong><em>a quantum-native</em> AI f ramework where quantum entanglement forms the core mechanism for coordinat ion. In eQMARL\, a quantum-entangled split critic links local observation encoders via entangled qubits\, removing the need for explicit data sharin g and reducing classical communication overhead. Joint quantum measurement s enable coordinated policy updates with far fewer centralized parameters. Experiments show that eQMARL improves convergence tiem and achieves highe r scores than classical and quantum baselines\, and reduces centralized pa rameters significantly compared to the split classical baseline. We conclu de the talk with an overview on important open problems in these areas\, a s well as some of our ongoing research activities.</p> END:VEVENT END:VCALENDAR