Presented By: QuantUM
QuantUM Speaker Series: Exploring Quantum Machine Learning: From Algorithms to Applications
Mohammad Aamir Sohail
Join QuantUM for a speaker series covering quantum machine learning and quantum algorithms! We'll have food beforehand starting at 5:30 (across the hall in the Don Meyer commons), and our speaker will commence at 6:00 PM in West Hall 340.
UMich PhD student Mohammad Aamir Sohail will speak on his research in quantum machine learning and quantum algorithms. The event is open to all, targeted to undergraduate students, and no background experience is required.
Abstract: In recent years, quantum machine learning has emerged as a promising approach for solving complex computational problems that challenge classical computers. In this talk, we will explore the motivation behind QML and introduce fundamental concepts that make quantum-enhanced learning distinctive and powerful. Starting with an accessible introduction to classical versus quantum machine learning, we will delve into the basics of variational quantum algorithms like the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). We will discuss how these algorithms can be applied to real-world optimization problems and design a quantum classifier using variational circuits.
UMich PhD student Mohammad Aamir Sohail will speak on his research in quantum machine learning and quantum algorithms. The event is open to all, targeted to undergraduate students, and no background experience is required.
Abstract: In recent years, quantum machine learning has emerged as a promising approach for solving complex computational problems that challenge classical computers. In this talk, we will explore the motivation behind QML and introduce fundamental concepts that make quantum-enhanced learning distinctive and powerful. Starting with an accessible introduction to classical versus quantum machine learning, we will delve into the basics of variational quantum algorithms like the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). We will discuss how these algorithms can be applied to real-world optimization problems and design a quantum classifier using variational circuits.
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