Quantum Machine Learning (QML)

Panel Webinar

Speakers and Panelists:

Baw Chng (AI/ML Co-Chair)	Chintan Oza (QIT Co-Chair)	Gokhale Jayanthi (eOTF President)	Prakash Ramachandran (QIT Co-Chair)

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.