Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing

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Special Presentation by Dr. Louis (K.-C.) Chen (Imperial College London, UK)

Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group

Date/Time: Thursday, 4 December 2025 @ 6 PM Eastern Time (3 PM Pacific Time)

PDH Certificate: while basic attendance is free, this course also offers one (1) Professional Development Hour (PDH) for a nominal fee; please choose the appropriate "Registration Fee" when registering; actual, verified real-time attendance required for PDH; additional terms and conditions apply.

Topic:

Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing 

Abstract:

We present a distributed quantum-classical framework that integrates photonic quantum neural networks (QNNs) with matrix product state (MPS) mapping to enable parameter-efficient training of classical neural networks. By leveraging universal linear-optical decompositions and photon-counting measurement statistics, the architecture generates neural parameters through a hybrid quantum-classical workflow. The framework demonstrates significant parameter compression while maintaining competitive performance and exhibits resilience to realistic photonic noise, underscoring its potential for scalable and near-term distributed quantum computing applications..

Speaker:

Dr. Louis (K.-C.) Chen received his M.Sc. and Ph.D. from Imperial College London, where he was affiliated with the Imperial Quantum Centre (QuEST). His research primarily focused on distributed quantum computing and devices for quantum information processing. He is currently a postdoctoral research associate at Imperial College London. Before pursuing his Ph.D., he worked as an R&D device engineer at TSMC, specializing in 3DIC and memory devices. In 2024, Dr. Chen won first prize in the Deloitte Quantum Challenge and third prize in the Pascal Blaise Quantum Challenge. In 2025, he received the IEEE Quantum Technology Conference (QTC) Distinguished QCE25 Technical Paper Award, including Best Paper in both the Quantum Application Track and the Photonic Track. His research interests include distributed quantum computing, quantum machine learning/AI, and quantum optimization problems. He also hosted the first workshop on quantum networked applications and protocols at Infocom and has presented on distributed quantum computing at major venues such as Infocom, ICASSP, ISCAS, IEEE QCE, QTML, IJCNN, QCNC, and SSCI.

Brochure (PDF): Webinar-AIML-2025-12-04-Chen-QNN-QPHO-Brochure.pdf