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IEEE Future Networks Artificial Intelligence and Machine Learning (AIML)
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About this event
IEEE Future Networks Artificial Intelligence and Machine Learning
Date: October 2, 2025
Time: 6:00 - 7:00 PM
Location: Virtual
Quantum Machine Learning: Quantum Architecture Search
Speaker: Dr. Samuel Yen-Chi Chen
Quantum Machine Learning (QML) stands at the cutting edge of computational intelligence, integrating quantum computing with classical machine learning to tackle complex problems beyond the reach of conventional methods. This talk will examine how QML harnesses quantum mechanical principles — including superposition, entanglement, and interference — to enable novel learning paradigms. Special emphasis will be placed on variational quantum circuits (VQCs) as a core building block for designing QML models on noisy intermediate-scale quantum (NISQ) hardware. In addition, I will introduce emerging techniques in Quantum Architecture Search (QAS), which automate the discovery and optimization of quantum circuit structures tailored for specific learning tasks. Drawing on our latest research, I will showcase applications where QML and QAS synergistically advance performance across multiple domains. The presentation will conclude by discussing the mutual reinforcement between artificial intelligence and quantum computing, outlining both the opportunities and key challenges that shape the future of QML.
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; additional terms and conditions apply.
Register here:
https://events.vtools.ieee.org/m/497031