Kim A. Nicoli
Kim A. Nicoli
Doctoral Researcher
Kim Nicoli is an associate researcher working in the Machine Learning Group at Technische Universität Berlin. Kim earned his Master’s degree in Physics of Complex Systems in 2017 from the University of Turin, Italy. In the second half of 2017 he has been a visiting researcher at NORDITA (Nordic Institute For Theoretical Physics) working in the field of active Brownian Motion. Since early 2018 Kim joined the Machine Learning group at TU Berlin. His research is now mainly focused on Machine Learning applications to lattice physics and quantum computing problems.
- Machine Learning
- Deep Generative Models
- Variational Inference
- Bayesian Inference
- Machine Learning applications to Lattice Field Theory and Quantum Computing (VQEs)
Kim Andrea Nicoli, Christopher Anders, Lena Funcke, Karl Jansen, Shinichi Nakajima
NeuLat: a toolbox for neural samplers in lattice field theories
Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
Kim Andrea Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kuhn, Klaus-Robert Muller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL divergence for Normalizing Flows
Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim A. Nicoli, Paolo Stornati, Rouven Koch, Miriam Büttner, Robert Okuła, Gorka Muñoz-Gil, Rodrigo A. Vargas-Hernández, Alba Cervera-Lierta, Juan Carrasquilla, Vedran Dunjko, Marylou Gabrié, Patrick Huembeli, Evert van Nieuwenburg, Filippo Vicentini, Lei Wang, Sebastian J. Wetzel, Giuseppe Carleo, Eliška Greplová, Roman Krems, Florian Marquardt, Michał Tomza, Maciej Lewenstein, Alexandre Dauphin