Quantum Machine Learning (QML)
Quantum Machine Learning (QML)
Panel Webinar
Speakers and Panelists:
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Baw Chng |
Chintan Oza |
Gokhale Jayanthi |
Prakash Ramachandran |
The rapid development of Artificial Intelligence and Machine Learning (AI/ML) following advancements such as ChatGPT and DeepSeek in 2023 and 2024 has increased the demand for computing capabilities beyond those provided by hyperconverged data centers. There is strong motivation and ongoing research to exploit quantum phenomena to enhance integrated intelligence in sensing, computing, networking, and communications.
The IEEE International Network Generations Roadmap (INGR) promotes the advancement of future networks through 15 working groups, including the AI/ML working group and the Quantum Information Technology (QIT) working group, which organize this series of panel webinars. These panel webinars will offer insights into the impact of quantum information science and technology on machine learning, with a focus on moving towards Agentic AI for various use cases, as the pace of innovation accelerates in the coming decades.
This panel webinar on Quantum Machine Learning (QML) will discuss classical machine learning approaches and corresponding use cases, and examine how quantum machine learning can significantly reduce the computation time to solve certain NP-hard problems. The differences between classical and quantum ML pipelines — including output states, inputs, learning models, and encoding/decoding methods — will be explored, along with techniques to analyze and interpret the results. The expert panel will present work covering classical models, such as supervised vector machines (SVM) and various neural networks (xNN), and outline the distinctions from their QML counterparts. They will also show utilities, tools, and code developments ranging from Qiskit to PennyLane, highlighting 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.
This panel webinar is co-hosted by the IEEE Future Networks Technical Community (FNTC) International Network Generations Roadmap (INGR) AI/ML Working Group and QIT Working Group, the eMerging Open Tech Foundation which focuses on Quantum and AI skills development, and the IEEE Philadelphia Section. 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.
Speakers and panelists' biographies below.
Date and Time
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Registration
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Craig Polk [c.polk@comsoc.org]
- Co-sponsored by eMerging Open Tech Foundation
- Starts 01 August 2025 12:00 AM UTC
- Ends 14 August 2025 03:55 PM UTC
- No Admission Charge
Speakers
Baw Chng of BAWMAN LLC
Biography:
Baw Chng co-chairs the IEEE International Network Generations Roadmap (INGR) Artificial Intelligence and Machine Learning (AI/ML) Working Group. He served as Editor of the AI/ML chapter of the INGR (2024, 2023, & 2022 editions), and is a co-author of the Security & Privacy chapter, the Standardization Building Blocks chapter, and the System Optimization chapter of the INGR. He has been awarded multiple patents in network and system architecture, security & authentication, mobility, and related fields. He has extensive experience working with and consulting for large national communication service providers and their strategic technology vendors on strategy development, engineering R&D, and Standards engagements. He also consults for the legal community as an Expert Witness.
Email:
Gokhale Jayanthi of eMerging Open Tech Foundation
Biography:
Gokhale Jayanthi is the President of eMerging Open Tech Foundation. Known as Prof. Gokhale, he was popular in Mumbai India & Silicon Valley as he spent early years (1984-2000) as Professor in CS/Electronics Dept. at Mumbai University. He started with Startup in Embedded electronics early before delivering JDK 1.2 for SUN in 2000. Later pursued his career as electronics and algorithm designer for various Industries to support cloud migration and currently a leader in delivering AI & Quantum ML for fortune 500 companies like L&T, Defense organizations in India. He did his Masters in CS at IITB and Bachelors in Electronics and communications from Osmania University in 80s.
Prakash Ramachandran of eMerging Open Tech Foundation
Biography:
Prakash Ramachandran INGR QIT/EdgeServicePlatform working group Co-chair since 2020: A serial entrepreneur with several startups in ICT and now retired and investing his time in emerging technologies skillset development and mentoring various groups in areas of AIML, Quantum and Semiconductor technology. Has wide experience working in Silicon Valley for Dell, Huawei, AT&T, Tandem computers. Was Independent Board of Director for OpenStack 2018-2020 and part of various Open Source initiatives under Linux Foundation. Was vice chair Silicon Valley chapter of IEEE 2017-2019.
Email:
Chintan Oza of Founder Institute
Biography:
Chintan Oza is a seasoned technology leader with over 25 years of experience in telecommunications and startups, holding degrees from IIT Mumbai, UC Berkeley, and Oxford University. He has driven strategic projects at Reliance and Tata Group, notably leading India’s fastest GSM and EVDO network rollouts. As Regional Director of Founder Institute in India, Chintan has mentored over 1,770 entrepreneurs in scaling tech ventures. Chintan serves on advisory boards for multiple startups guiding technology scaling and market entry. He is an active angel investor and venture advisor with networks like FI Venture Network and Loyal VC. His expertise spans blockchain, IoT, and edge computing, contributing to India’s first smart city GIFT City project. A recognized thought leader, he has delivered two TEDx talks and authored four books. Chintan holds certifications in Scrum, PMP, and Smart Cities, and has received awards like NASDAQ’s Top Mentor in 2022.