Welcome to our new Quantum Information Seminar Series, hosted by the University of British Columbia Quantum Club!
We offer monthly talks on cutting-edge research in quantum information science and quantum computing.
Abstract:
Quantum machine learning (QML) brings together the principles of quantum computing and the goals of modern machine learning, raising the question of whether quantum devices can offer new capabilities for learning from data. In this talk, I will introduce the core ideas behind QML, beginning with a brief historical perspective and the essential definitions and paradigms. We will then explore concrete quantum models, including quantum neural networks (QNNs) and quantum support vector machines (QSVMs), and examine in detail how these models are trained and optimized. We will also shift our focus to quantum data, exploring how learning directly from quantum systems opens up entirely new prospects for QML. The talk ends with an honest look at what currently prevents QML from scaling and what researchers are doing to address these challenges.
Bio:
Jonas is a final-year Ph.D. candidate in the Department of Computer Science and the Institute of Applied Mathematics at UBC. Originally from Germany, he earned his Bachelor’s and Master’s degrees in Computer Science, as well as a Master’s in Autonomous Systems, from the Technical University of Darmstadt. His current research explores the intersection of quantum computing, machine learning, and optimization. Throughout his Ph.D., Jonas has collaborated with leading research teams at the German Aerospace Center (DLR), Los Alamos National Laboratory, and BMW. When he's not juggling the final pieces of his Ph.D. research, you can find him rock climbing and exploring the outdoors of the Pacific Northwest.
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2026-03-27T17:00:002026-03-27T18:00:00Quantum & Machine Learning: A Powerful Combination? Possibilities, Methods, and LimitationsEvent Information:
Welcome to our new Quantum Information Seminar Series, hosted by the University of British Columbia Quantum Club!
We offer monthly talks on cutting-edge research in quantum information science and quantum computing.
Abstract:
Quantum machine learning (QML) brings together the principles of quantum computing and the goals of modern machine learning, raising the question of whether quantum devices can offer new capabilities for learning from data. In this talk, I will introduce the core ideas behind QML, beginning with a brief historical perspective and the essential definitions and paradigms. We will then explore concrete quantum models, including quantum neural networks (QNNs) and quantum support vector machines (QSVMs), and examine in detail how these models are trained and optimized. We will also shift our focus to quantum data, exploring how learning directly from quantum systems opens up entirely new prospects for QML. The talk ends with an honest look at what currently prevents QML from scaling and what researchers are doing to address these challenges.
Bio:
Jonas is a final-year Ph.D. candidate in the Department of Computer Science and the Institute of Applied Mathematics at UBC. Originally from Germany, he earned his Bachelor’s and Master’s degrees in Computer Science, as well as a Master’s in Autonomous Systems, from the Technical University of Darmstadt. His current research explores the intersection of quantum computing, machine learning, and optimization. Throughout his Ph.D., Jonas has collaborated with leading research teams at the German Aerospace Center (DLR), Los Alamos National Laboratory, and BMW. When he's not juggling the final pieces of his Ph.D. research, you can find him rock climbing and exploring the outdoors of the Pacific Northwest.
Event Location:
HENN 318
