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DESCRIPTION:Quantum Machine Learning (QML)\n\nPanel Webinar\n\nSpeakers and
  Panelists:\n\n[]	[]	[]	[]\n\nBaw Chng\n(AI/ML Co-Chair)\n\nChintan Oza\n(
 QIT Co-Chair)\n\nGokhale Jayanthi\n(eOTF President)\n\nPrakash Ramachandra
 n\n(QIT Co-Chair)\n\nThe rapid development of Artificial Intelligence and 
 Machine Learning (AI/ML) following advancements such as ChatGPT and DeepSe
 ek in 2023 and 2024 has increased the demand for computing capabilities be
 yond those provided by hyperconverged data centers. There is strong motiva
 tion and ongoing research to exploit quantum phenomena to enhance integrat
 ed intelligence in sensing\, computing\, networking\, and communications.\
 n\nThe IEEE [International Network Generations Roadmap (INGR)](https://fut
 urenetworks.ieee.org/roadmap) promotes the advancement of future networks 
 through 15 working groups\, including the [AI/ML working group](https://fu
 turenetworks.ieee.org/roadmap/aiml-working-group) and the [Quantum Informa
 tion Technology (QIT) working group](https://futurenetworks.ieee.org/roadm
 ap/qit-working-group)\, which organize this series of panel webinars. Thes
 e panel webinars will offer insights into the impact of quantum informatio
 n science and technology on machine learning\, with a focus on moving towa
 rds Agentic AI for various use cases\, as the pace of innovation accelerat
 es in the coming decades.\n\nThis panel webinar on Quantum Machine Learnin
 g (QML) will discuss classical machine learning approaches and correspondi
 ng use cases\, and examine how quantum machine learning can significantly 
 reduce the computation time to solve certain NP-hard problems. The differe
 nces between classical and quantum ML pipelines — including output state
 s\, inputs\, learning models\, and encoding/decoding methods — will be e
 xplored\, along with techniques to analyze and interpret the results. The 
 expert panel will present work covering classical models\, such as supervi
 sed vector machines (SVM) and various neural networks (xNN)\, and outline 
 the distinctions from their QML counterparts. They will also show utilitie
 s\, tools\, and code developments ranging from Qiskit to PennyLane\, highl
 ighting the application of QML algorithms to natural language processing (
 NLP)\, large language model (LLM)\, and multimodal learning. Additionally\
 , the discussion will cover relevant APIs\, Bra-ket notation\, and matrix 
 representations used in quantum inference.\n\nThis panel webinar is co-hos
 ted by the IEEE Future Networks Technical Community ([FNTC](https://future
 networks.ieee.org/)) International Network Generations Roadmap ([INGR](htt
 ps://futurenetworks.ieee.org/roadmap)) [AI/ML Working Group](https://futur
 enetworks.ieee.org/roadmap/aiml-working-group) and [QIT Working Group](htt
 ps://futurenetworks.ieee.org/roadmap/qit-working-group)\, the [eMerging Op
 en Tech Foundation](https://www.eotf.in/eotf/) which focuses on Quantum an
 d AI skills development\, and the [IEEE Philadelphia Section](https://phil
 adelphia.ieee.org). This panel webinar will be recorded and made available
  to registered participants. Registration is open for those interested in 
 attending and participating in live discussions.\n\nSpeakers and panelists
 &#39; biographies below.\n\nCo-sponsored by: eMerging Open Tech Foundation\n\n
 Speaker(s): Baw Chng\, Gokhale Jayanthi\, Prakash Ramachandran\, Chintan O
 za\n\nVirtual: https://events.vtools.ieee.org/m/495150
LOCATION:Virtual: https://events.vtools.ieee.org/m/495150
ORGANIZER:cloud24x7@ieee.org
SEQUENCE:175
SUMMARY:Quantum Machine Learning (QML)
URL;VALUE=URI:https://events.vtools.ieee.org/m/495150
X-ALT-DESC:Description: &lt;br /&gt;&lt;h1&gt;&lt;strong&gt;Quantum Machine Learning (QML)&lt;/s
 trong&gt;&lt;/h1&gt;\n&lt;h2&gt;Panel Webinar&lt;/h2&gt;\n&lt;p&gt;Speakers and Panelists:&lt;/p&gt;\n&lt;tabl
 e style=&quot;border-collapse: collapse\; width: 100%\;&quot; border=&quot;1&quot;&gt;&lt;colgroup&gt;&lt;
 col style=&quot;width: 25%\;&quot;&gt;&lt;col style=&quot;width: 25%\;&quot;&gt;&lt;col style=&quot;width: 25%\
 ;&quot;&gt;&lt;col style=&quot;width: 25%\;&quot;&gt;&lt;/colgroup&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td style=&quot;text-a
 lign: center\; vertical-align: bottom\;&quot;&gt;&lt;img src=&quot;https://events.vtools.i
 eee.org/vtools_ui/media/display/3207ea46-b161-4b87-a669-9f2f07fdbccf&quot; alt=
 &quot;&quot; width=&quot;191&quot; height=&quot;255&quot;&gt;&lt;/td&gt;\n&lt;td style=&quot;text-align: center\; vertica
 l-align: bottom\;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/medi
 a/display/9759af76-ffbb-4d7f-801b-cd1ff7ef7bec&quot; alt=&quot;&quot; width=&quot;255&quot; height=
 &quot;255&quot;&gt;&lt;/td&gt;\n&lt;td style=&quot;text-align: center\; vertical-align: bottom\;&quot;&gt;&lt;im
 g src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/22e3755b-cdc
 0-471f-9a10-576d48376689&quot; alt=&quot;&quot; width=&quot;255&quot; height=&quot;255&quot;&gt;&lt;/td&gt;\n&lt;td style
 =&quot;text-align: center\; vertical-align: bottom\;&quot;&gt;&lt;img src=&quot;https://events.
 vtools.ieee.org/vtools_ui/media/display/8cba2b2e-09cc-4c20-a31a-97a2fe96e4
 63&quot; alt=&quot;&quot; width=&quot;224&quot; height=&quot;255&quot;&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td style=&quot;text-al
 ign: center\; vertical-align: top\;&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Baw Chng&lt;/strong&gt;&lt;br&gt;(AI
 /ML Co-Chair)&lt;/p&gt;\n&lt;/td&gt;\n&lt;td style=&quot;text-align: center\; vertical-align: 
 bottom\;&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Chintan Oza&lt;/strong&gt;&lt;br&gt;(QIT Co-Chair)&lt;/p&gt;\n&lt;/td&gt;\n
 &lt;td style=&quot;text-align: center\; vertical-align: bottom\;&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Gok
 hale Jayanthi&lt;/strong&gt;&lt;br&gt;(eOTF President)&lt;/p&gt;\n&lt;/td&gt;\n&lt;td style=&quot;text-ali
 gn: center\; vertical-align: bottom\;&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Prakash Ramachandran&lt;/
 strong&gt;&lt;br&gt;(QIT Co-Chair)&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;\n&lt;p class=
 &quot;MsoNormal&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;The rapid development of Art
 ificial Intelligence and Machine Learning (AI/ML) following advancements s
 uch as ChatGPT and DeepSeek in 2023 and 2024 has increased the demand for 
 computing capabilities beyond those provided by hyperconverged data center
 s. There is strong motivation and ongoing research to exploit quantum phen
 omena to enhance integrated intelligence in sensing\, computing\, networki
 ng\, and communications.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;The IEEE &lt;a href=&quot;https
 ://futurenetworks.ieee.org/roadmap&quot; target=&quot;INGR&quot; rel=&quot;noopener&quot;&gt;Internati
 onal Network Generations Roadmap (INGR)&lt;/a&gt; promotes the advancement of fu
 ture networks through 15 working groups\, including the &lt;a href=&quot;https://f
 uturenetworks.ieee.org/roadmap/aiml-working-group&quot; target=&quot;FN-AIML&quot; rel=&quot;n
 oopener&quot;&gt;AI/ML working group&lt;/a&gt; and the &lt;a href=&quot;https://futurenetworks.i
 eee.org/roadmap/qit-working-group&quot; target=&quot;FN-QIT&quot; rel=&quot;noopener&quot;&gt;Quantum 
 Information Technology (QIT) working group&lt;/a&gt;\, which organize this serie
 s of panel webinars. These panel webinars will offer insights into the imp
 act of quantum information science and technology on machine learning\, wi
 th a focus on moving towards Agentic AI for various use cases\, as the pac
 e of innovation accelerates in the coming decades.&lt;/p&gt;\n&lt;p class=&quot;MsoNorma
 l&quot;&gt;This panel webinar on &lt;strong&gt;Quantum Machine Learning (QML)&lt;/strong&gt; w
 ill discuss classical machine learning approaches and corresponding use ca
 ses\, and examine how quantum machine learning can significantly reduce th
 e computation time to solve certain NP-hard problems. The differences betw
 een classical and quantum ML pipelines &amp;mdash\; including output states\, 
 inputs\, learning models\, and encoding/decoding methods &amp;mdash\; will be 
 explored\, along with techniques to analyze and interpret the results. The
  expert panel will present work covering classical models\, such as superv
 ised vector machines (SVM) and various neural networks (xNN)\, and outline
  the distinctions from their QML counterparts. They will also show utiliti
 es\, tools\, and code developments ranging from Qiskit to PennyLane\, high
 lighting the application of QML algorithms to natural language processing 
 (NLP)\, large language model (LLM)\, and multimodal learning. Additionally
 \, the discussion will cover relevant APIs\, Bra-ket notation\, and matrix
  representations used in quantum inference.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;This
  panel webinar is co-hosted by the IEEE Future Networks Technical Communit
 y (&lt;a href=&quot;https://futurenetworks.ieee.org/&quot; target=&quot;_blank&quot; rel=&quot;noopene
 r&quot;&gt;FNTC&lt;/a&gt;) International Network Generations Roadmap (&lt;a href=&quot;https://f
 uturenetworks.ieee.org/roadmap&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;INGR&lt;/a&gt;) &lt;
 a href=&quot;https://futurenetworks.ieee.org/roadmap/aiml-working-group&quot; target
 =&quot;FN-AIML&quot; rel=&quot;noopener&quot;&gt;AI/ML Working Group&lt;/a&gt; and &lt;a href=&quot;https://fut
 urenetworks.ieee.org/roadmap/qit-working-group&quot; target=&quot;FN-QIT&quot; rel=&quot;noope
 ner&quot;&gt;QIT Working Group&lt;/a&gt;\, the&amp;nbsp\;&lt;a href=&quot;https://www.eotf.in/eotf/&quot;
  target=&quot;eOTF&quot; rel=&quot;noopener&quot;&gt;eMerging Open Tech Foundation&lt;/a&gt; which focu
 ses on Quantum and AI skills development\, and the &lt;a href=&quot;https://philad
 elphia.ieee.org&quot; target=&quot;PhiladelphiaSection&quot; rel=&quot;noopener&quot;&gt;IEEE Philadel
 phia Section&lt;/a&gt;. This panel webinar will be recorded and made available t
 o registered participants. Registration is open for those interested in at
 tending and participating in live discussions.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;S
 peakers and panelists&#39; biographies below.&lt;/p&gt;
END:VEVENT
END:VCALENDAR