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DTSTAMP:20260124T044310Z
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DTSTART;TZID=America/New_York:20251204T180000
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DESCRIPTION:[Distributed Quantum Neural Networks on Distributed Photonic Qu
 antum Computing]\n\nSpecial Presentation by Dr. Louis (K.-C.) Chen (Imperi
 al College London\, UK)\n\nHosted by the Future Networks Artificial Intell
 igence &amp; Machine Learning (AIML) Working Group\n\nDate/Time: Thursday\, 4 
 December 2025 @ 6 PM Eastern Time (3 PM Pacific Time)\n\nPDH Certificate: 
 while basic attendance is free\, this course also offers one (1) Professio
 nal Development Hour (PDH) for a nominal fee\; please choose the appropria
 te &quot;Registration Fee&quot; when registering\; actual\, verified real-time atten
 dance required for PDH\; additional terms and conditions apply.\n\nTopic:\
 n\nDistributed Quantum Neural Networks on Distributed Photonic Quantum Com
 puting\n\nAbstract:\n\nWe present a distributed quantum-classical framewor
 k that integrates photonic quantum neural networks (QNNs) with matrix prod
 uct state (MPS) mapping to enable parameter-efficient training of classica
 l neural networks. By leveraging universal linear-optical decompositions a
 nd photon-counting measurement statistics\, the architecture generates neu
 ral parameters through a hybrid quantum-classical workflow. The framework 
 demonstrates significant parameter compression while maintaining competiti
 ve performance and exhibits resilience to realistic photonic noise\, under
 scoring its potential for scalable and near-term distributed quantum compu
 ting applications..\n\nSpeaker:\n\n[]\nDr. Louis (K.-C.) Chen received his
  M.Sc. and Ph.D. from Imperial College London\, where he was affiliated wi
 th the Imperial Quantum Centre (QuEST). His research primarily focused on 
 distributed quantum computing and devices for quantum information processi
 ng. He is currently a postdoctoral research associate at Imperial College 
 London. Before pursuing his Ph.D.\, he worked as an R&amp;D device engineer at
  TSMC\, specializing in 3DIC and memory devices. In 2024\, Dr. Chen won fi
 rst prize in the Deloitte Quantum Challenge and third prize in the Pascal 
 Blaise Quantum Challenge. In 2025\, he received the IEEE Quantum Technolog
 y Conference (QTC) Distinguished QCE25 Technical Paper Award\, including B
 est Paper in both the Quantum Application Track and the Photonic Track. Hi
 s research interests include distributed quantum computing\, quantum machi
 ne learning/AI\, and quantum optimization problems. He also hosted the fir
 st workshop on quantum networked applications and protocols at Infocom and
  has presented on distributed quantum computing at major venues such as In
 focom\, ICASSP\, ISCAS\, IEEE QCE\, QTML\, IJCNN\, QCNC\, and SSCI.\n\nBro
 chure (PDF): [Webinar-AIML-2025-12-04-Chen-QNN-QPHO-Brochure.pdf](https://
 drive.google.com/file/d/1zBokwvnJvPDMsq_eixogw8YsY48EYpZv/view)\n\nCo-spon
 sored by: Future Networks Artificial Intelligence &amp; Machine Learning (AIML
 ) Working Group\n\nVirtual: https://events.vtools.ieee.org/m/500584
LOCATION:Virtual: https://events.vtools.ieee.org/m/500584
ORGANIZER:baw@ieee.org
SEQUENCE:55
SUMMARY:Distributed Quantum Neural Networks on Distributed Photonic Quantum
  Computing
URL;VALUE=URI:https://events.vtools.ieee.org/m/500584
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 &lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;Special Presentation by&lt;
 strong&gt; Dr. Louis (K.-C.) Chen (Imperial College London\, UK)&lt;/strong&gt;&lt;/p&gt;
 \n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;Hosted by the Future N
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 @ 6 PM Eastern Time (3 PM Pacific Time)&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;Mso
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 H-TW\; mso-bidi-language: AR-SA\;&quot;&gt;PDH Certificate&lt;/span&gt;: &lt;/em&gt;&lt;/strong&gt;&lt;
 em&gt;while basic attendance is free\, this course also offers one (1) Profes
 sional Development Hour (PDH) for a nominal fee\; please choose the approp
 riate &quot;Registration Fee&quot; when registering\; actual\, verified real-time at
 tendance required for PDH\; additional terms and conditions apply.&lt;/em&gt;&lt;/s
 pan&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;spa
 n style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;Topic&lt;/span&gt;&lt;/u&gt;&lt;
 /strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copperplate\
 ;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-s
 ize: 21.333334px\;&quot;&gt;Distributed Quantum Neural Networks on Distributed Pho
 tonic Quantum Computing&lt;/span&gt;&lt;span style=&quot;font-size: 16pt\;&quot;&gt;&amp;nbsp\;&lt;/spa
 n&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;
 &lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;Abstract&lt;/
 span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Co
 pperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;We present a dist
 ributed quantum-classical framework that integrates photonic quantum neura
 l networks (QNNs) with matrix product state (MPS) mapping to enable parame
 ter-efficient training of classical neural networks. By leveraging univers
 al linear-optical decompositions and photon-counting measurement statistic
 s\, the architecture generates neural parameters through a hybrid quantum-
 classical workflow. The framework demonstrates significant parameter compr
 ession while maintaining competitive performance and exhibits resilience t
 o realistic photonic noise\, underscoring its potential for scalable and n
 ear-term distributed quantum computing applications..&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;spa
 n style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;&lt;u&gt;Speaker&lt;/u&gt;:&lt;/
 span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;table style=&quot;border-collapse: collapse\; width: 100%\
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 &quot; alt=&quot;&quot; width=&quot;173&quot; height=&quot;200&quot;&gt;&lt;/td&gt;\n&lt;td&gt;\n&lt;p class=&quot;MsoNormal&quot; style=
 &quot;margin-top: 6.0pt\;&quot;&gt;&lt;strong&gt;Dr. &lt;/strong&gt;&lt;strong&gt;Louis (K.-C.) Chen&lt;/str
 ong&gt; received his M.Sc. and Ph.D. from Imperial College London\, where he 
 was affiliated with the Imperial Quantum Centre (QuEST). His research prim
 arily focused on distributed quantum computing and devices for quantum inf
 ormation processing. He is currently a postdoctoral research associate at 
 Imperial College London. Before pursuing his Ph.D.\, he worked as an R&amp;amp
 \;D device engineer at TSMC\, specializing in 3DIC and memory devices. In 
 2024\, Dr. Chen won first prize in the Deloitte Quantum Challenge and thir
 d prize in the Pascal Blaise Quantum Challenge. In 2025\, he received the 
 IEEE Quantum Technology Conference (QTC) Distinguished QCE25 Technical Pap
 er Award\, including Best Paper in both the Quantum Application Track and 
 the Photonic Track. His research interests include distributed quantum com
 puting\, quantum machine learning/AI\, and quantum optimization problems. 
 He also hosted the first workshop on quantum networked applications and pr
 otocols at Infocom and has presented on distributed quantum computing at m
 ajor venues such as Infocom\, ICASSP\, ISCAS\, IEEE QCE\, QTML\, IJCNN\, Q
 CNC\, and SSCI.&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;
 &lt;strong&gt;Brochure (PDF)&lt;/strong&gt;: &lt;strong&gt;&lt;a id=&quot;gmail-ow153&quot; class=&quot;gmail-
 pastedDriveLink-1&quot; title=&quot;Distributed Quantum Neural Networks on Distribut
 ed Photonic Quantum Computing&quot; href=&quot;https://drive.google.com/file/d/1zBok
 wvnJvPDMsq_eixogw8YsY48EYpZv/view&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;Webinar-
 AIML-2025-12-04-Chen-QNN-QPHO-Brochure.pdf&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;
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