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:20260124T044837Z UID:4D4C43E7-F928-4349-9512-0AD17B997796 DTSTART;TZID=America/New_York:20251002T180000 DTEND;TZID=America/New_York:20251002T190000 DESCRIPTION:Special Presentation by Dr. Samuel Yen-Chi Chen (Wells Fargo\, USA)\n\nCo-Hosted by the Future Networks AI/ML and QIT working groups\n\nD ate/Time: Thursday\, 2 October 2025 @ 6 PM EDT\n\nPDH Certificate: while b asic attendance is free\, this course also offers one (1) Professional Dev elopment Hour (PDH) for a nominal fee\; please choose the appropriate "Reg istration Fee" when registering\; actual\, verified real-time attendance r equired for PDH\; additional terms and conditions apply.\n\nTopic:\n\nQuan tum Machine Learning: Quantum Architecture Search\n\nAbstract:\n\nQuantum Machine Learning (QML) stands at the cutting edge of computational intelli gence\, integrating quantum computing with classical machine learning to t ackle complex problems beyond the reach of conventional methods. This talk will examine how QML harnesses quantum mechanical principles — includin g superposition\, entanglement\, and interference — to enable novel lear ning paradigms. Special emphasis will be placed on variational quantum cir cuits (VQCs) as a core building block for designing QML models on noisy in termediate-scale quantum (NISQ) hardware. In addition\, I will introduce e merging techniques in Quantum Architecture Search (QAS)\, which automate t he discovery and optimization of quantum circuit structures tailored for s pecific learning tasks. Drawing on our latest research\, I will showcase a pplications where QML and QAS synergistically advance performance across m ultiple domains. The presentation will conclude by discussing the mutual r einforcement between artificial intelligence and quantum computing\, outli ning both the opportunities and key challenges that shape the future of QM L\n\nSpeaker:\n\n[]\nSamuel Yen-Chi Chen is a Lead Research Scientist at W ells Fargo\, specializing in Quantum Machine Learning (QML)\, Reinforcemen t Learning\, and Neural Architecture Search. With a Ph.D. in Physics and e xtensive experience across quantum AI\, high-performance computing\, and a lgorithmic design\, he has published over 60 papers in IEEE\, APS\, IOP\, and leading AI conferences. Samuel is best known for his work on Quantum R einforcement Learning (QRL)\, Quantum Long Short-Term Memory (QLSTM)\, Qua ntum Fast Weight Programming\, and the Differentiable Quantum Architecture Search (DiffQAS) framework. He has organized multiple workshops and tutor ials at IEEE ICASSP\, ISCAS\, FUZZ\, QCE\, GLOBECOM\,WCNC\, ICC\, and IJCN N\, and continues to push the boundaries of hybrid quantum-classical intel ligence. His current research explores self-evolving quantum agents and st ructure-aware QNN design for time-series learning\, quantum reinforcement learning\, and communication systems.\n\nBrochure (PDF): [Webinar-AIML-202 5-10-02-Chen-Quantum-Architecture-Search-Brochure.pdf](https://www.google. com/url?q=https://drive.google.com/file/d/1WpaCNv4Z0aYnLFT2KABkEnC5NmWcqO9 l/view?usp%3Dshare_link&sa=D&source=calendar&ust=1758927552039507&usg=AOvV aw1kwHAsBNx1yTPj4wb5ph99)\n\nCo-sponsored by: Future Networks Artificial I ntelligence & Machine Learning (AIML) Working Group\n\nVirtual: https://ev ents.vtools.ieee.org/m/497031 LOCATION:Virtual: https://events.vtools.ieee.org/m/497031 ORGANIZER:baw@ieee.org SEQUENCE:43 SUMMARY:Quantum Machine Learning (QML): Quantum Architecture Search (QAS) URL;VALUE=URI:https://events.vtools.ieee.org/m/497031 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-top: .25in \;"><img src="https://events.vtools.ieee.org/vtools_ui/media/display/313b7 d9c-559d-400f-b617-662e5d87ce36" width="750" height="197"></p>\n<p class=" MsoNormal" style="margin-top: 12.0pt\;">Special Presentation by<strong> Dr . Samuel Yen-Chi Chen (Wells Fargo\, USA)</strong></p>\n<p class="MsoNorma l" style="margin-top: 12.0pt\;">Co-Hosted by the Future Networks<strong> A I/ML </strong>and<strong> QIT working groups</strong></p>\n<p class="MsoNo rmal" style="margin-top: 12.0pt\;"><strong><span style="font-size: 14.0pt\ ; font-family: Copperplate\; mso-fareast-font-family: PMingLiU\; mso-farea st-theme-font: minor-fareast\; mso-bidi-font-family: Arial\; mso-bidi-them e-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-T W\; mso-bidi-language: AR-SA\;">Date/Time</span></strong><span style="font -size: 12.0pt\; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso- fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">: <strong>Thursday\, 2 October 2025</strong><strong> @ 6 PM EDT</strong></span></p>\n<p class= "MsoNormal" style="margin-top: 12.0pt\;"><span style="font-size: 12.0pt\; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; m so-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: Arial\; mso-bid i-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language : ZH-TW\; mso-bidi-language: AR-SA\;"><em><strong><span style="font-size: 14.0pt\; font-family: Copperplate\; mso-fareast-font-family: PMingLiU\; ms o-fareast-theme-font: minor-fareast\; mso-bidi-font-family: Arial\; mso-bi di-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-languag e: ZH-TW\; mso-bidi-language: AR-SA\;">PDH Certificate</span>:</strong> wh ile basic attendance is free\, this course also offers one (1) Professiona l Development Hour (PDH) for a nominal fee\; please choose the appropriate "Registration Fee" when registering\; actual\, verified real-time attenda nce required for PDH\; additional terms and conditions apply.</em></span>< /p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span sty le="font-size: 16.0pt\; font-family: Copperplate\;">Topic</span></u></stro ng><strong><span style="font-size: 16.0pt\; font-family: Copperplate\;">:< /span></strong></p>\n<p class="MsoNormal"><strong><span style="font-size: 21.333334px\;">Quantum Machine Learning: Quantum Architecture Search</span ></strong></p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong>< u><span style="font-size: 16.0pt\; font-family: Copperplate\;">Abstract</s pan></u></strong><strong><span style="font-size: 16.0pt\; font-family: Cop perplate\;">:</span></strong></p>\n<p class="MsoNormal">Quantum Machine Le arning (QML) stands at the cutting edge of computational intelligence\, in tegrating quantum computing with classical machine learning to tackle comp lex problems beyond the reach of conventional methods. This talk will exam ine how QML harnesses quantum mechanical principles &mdash\; including sup erposition\, entanglement\, and interference &mdash\; to enable novel lear ning paradigms. Special emphasis will be placed on variational quantum cir cuits (VQCs) as a core building block for designing QML models on noisy in termediate-scale quantum (NISQ) hardware. In addition\, I will introduce e merging techniques in Quantum Architecture Search (QAS)\, which automate t he discovery and optimization of quantum circuit structures tailored for s pecific learning tasks. Drawing on our latest research\, I will showcase a pplications where QML and QAS synergistically advance performance across m ultiple domains. The presentation will conclude by discussing the mutual r einforcement between artificial intelligence and quantum computing\, outli ning both the opportunities and key challenges that shape the future of QM L</p>\n<p><strong><span style="font-size: 16.0pt\; font-family: Copperplat e\;"><u>Speaker</u>:</span></strong></p>\n<table style="border-collapse: c ollapse\; width: 100%\;" border="1"><colgroup><col style="width: 16.122841 %\;"><col style="width: 83.78119%\;"></colgroup>\n<tbody>\n<tr>\n<td><img src="https://events.vtools.ieee.org/vtools_ui/media/display/5c9ad750-3e8c- 4ae5-9d65-4d4198cc1237" alt="" width="199" height="205"></td>\n<td>\n<p cl ass="MsoNormal" style="margin-top: 6.0pt\;"><strong>Samuel Yen-Chi Chen</s trong> is a Lead Research Scientist at Wells Fargo\, specializing in Quant um Machine Learning (QML)\, Reinforcement Learning\, and Neural Architectu re Search. With a Ph.D. in Physics and extensive experience across quantum AI\, high-performance computing\, and algorithmic design\, he has publish ed over 60 papers in IEEE\, APS\, IOP\, and leading AI conferences. Samuel is best known for his work on Quantum Reinforcement Learning (QRL)\, Quan tum Long Short-Term Memory (QLSTM)\, Quantum Fast Weight Programming\, and the Differentiable Quantum Architecture Search (DiffQAS) framework. He ha s organized multiple workshops and tutorials at IEEE ICASSP\, ISCAS\, FUZZ \, QCE\, GLOBECOM\,WCNC\, ICC\, and IJCNN\, and continues to push the boun daries of hybrid quantum-classical intelligence. His current research expl ores self-evolving quantum agents and structure-aware QNN design for time- series learning\, quantum reinforcement learning\, and communication syste ms.</p>\n</td>\n</tr>\n</tbody>\n</table>\n<p>&nbsp\;</p>\n<p><strong>Broc hure (PDF)</strong>: <a href="https://www.google.com/url?q=https://drive.g oogle.com/file/d/1WpaCNv4Z0aYnLFT2KABkEnC5NmWcqO9l/view?usp%3Dshare_link&a mp\;sa=D&amp\;source=calendar&amp\;ust=1758927552039507&amp\;usg=AOvVaw1kw HAsBNx1yTPj4wb5ph99" target="_blank" rel="noopener">Webinar-AIML-2025-10-0 2-Chen-Quantum-Architecture-Search-Brochure.pdf</a>&nbsp\;</p> END:VEVENT END:VCALENDAR