SAMOP 2023 – scientific programme

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11:00	QI 3.1	Invited Talk: Characterising quantum device variability with machine learning — •Natalia Ares

11:30	QI 3.2	The application of quantum neural networks in function approximation — •David Kreplin and Marco Roth

11:45	QI 3.3	Parameterized quantum circuits for reinforcement learning of classical rare dynamics — Alissa Wilms, •Laura Ohff, Andrea Skolik, David A. Reiss, Sumeet Khatri, and Jens Eisert

12:00	QI 3.4	Optimal storage capacity of quantum Hopfield neural networks — •Lukas Bödeker, Eliana Fiorelli, and Markus Müller

12:15	QI 3.5	Quantum kernel methods for regression — •Jan Schnabel

12:30	QI 3.6	Renormalisation through the lens of QCNNs — •Nathan A. McMahon, Petr Zapletal, and Michael J. Hartmann

12:45	QI 3.7	Quantum Gaussian Processes for Bayesian Optimization — •Frederic Rapp and Marco Roth