Machine Learning in Quantum Sciences

Anna Dawid just transitioned from a research fellow at the Flatiron Institute, New York, to an assistant professor at Leiden University. She holds a PhD in quantum physics awarded by the University of Warsaw and the Institute of Photonic Sciences (ICFO). Her research spans interpretable ML for scientific discovery, quantum simulations, and foundations of deep learning.

Julian Arnold is a theoretical physicist working at the interface between the quantum sciences, information theory, and machine learning (ML). His research includes the design of methods for the automated detection of phase transitions and the application of differentiable programming to solve inverse design problems in quantum many-body physics.

Borja Requena develops ML algorithms for scientific and engineering applications. He received his PhD at ICFO in 2024 with contributions spanning multiple fields, from quantum to biophysics. Borja has experience in deep-tech and AI companies such as Axiomatic AI, Xanadu Quantum Technologies, and Telefonica R&D, and he has been highly ranked in ML and quantum computing competitions.

Alexander Gresch (PhD student at the universities of Düsseldorf and Hamburg) is a theoretical physicist specializing in mathematical and machine learning methods in the context of quantum technologies. This includes, in particular, the efficient and accurate read-out of hybrid quantum algorithms and the role of quantum data for machine learning.

Marcin Płodzień (PhD, 2014, Jagiellonian University) is Research Fellow at ICFO, specializing in theoretical physics with a focus on many-body quantum systems and quantum information theory. His research interests include quantum simulators and quantum metrology, entanglement and Bell correlations, quantum reservoir computing, and applications of ML to quantum mechanics.

Kaelan Donatella is a Franco-Irish physicist trained at Ecole Normale Supérieure and the University of Paris. His interests range from quantum computing to the history and philosophy of science, with recent work being focused on analog computing for artificial intelligence.

Kim A. Nicoli is a postdoctoral researcher at the Helmholtz Institute for Radiation and Nuclear Physics and the University of Bonn. He got his PhD in ML from Technical University of Berlin in 2023. His research interests extend across probabilistic modeling, variational inference, generative models, lattice quantum field theory, quantum computing, and neuromorphic computing.

Paolo Stornati is a postdoctoral researcher in quantum simulation and quantum many-body theory. He has a deep interest in the development of novel numerical tools to study exotic phases of matter and lattice gauge theories.

Rouven Koch is a doctoral researcher at Aalto University, working at the intersection of condensed matter theory and ML. His research focuses on the combination of theory and experiments with the help of AI. Personally, he is interested in daily-life applications of AI.

Miriam Büttner earned her MSc in molecular science at the Friedrich-Alexander University of Erlangen-Nuremberg. In 2017, she went to Shenzhen, China, for an elective Master's project on ML in quantum chemistry and has since then been growing her ML knowledge. She is currently doing her PhD in many-body physics.

Robert Okuła is a PhD student interested in all things quantum, especially quantum cryptography and quantum Darwinism. He considers ML to be a useful tool in that regard.

Gorka Muñoz-Gil is a Marie Skłodowska-Curie fellow in Innsbruck University. Before, he received his PhD at ICFO (Spain) for the study of stochastic processes and their connection to ML. His research focuses on the application of ML techniques to a variety of topics, with a special interest in interpretable solutions.