Photo by Ola Sierant
Castle Square, Warsaw
Dr Anna Dawid
Quantum physics & machine learning scientist,
theatre and games enthusiast.
Hi! I’m an assistant professor at the Leiden Institute of Advanced Computer Science (LIACS) at Leiden University in the Netherlands and a principal investigator in the aQa group. I am happily playing with interpretable machine learning for science, ultracold platforms for quantum simulations, and theory of machine learning. My scientific passion is molding automated approaches into a unique new scientific lens and looking through it at established difficult quantum problems.
Sounds interesting? I’m looking for a postdoc! 🙂
Before joining aQa, I was a research fellow at the Center of Computational Quantum Physics of the Flatiron Institute in New York. In 2022, I defended my joint Ph.D. in physics and photonics under the supervision of Prof. Michał Tomza (Faculty of Physics, University of Warsaw, Poland) and Prof. Maciej Lewenstein (ICFO – The Institute of Photonic Sciences, Spain). Before that, I did my MSc in quantum chemistry and BSc in biotechnology at the University of Warsaw.
I am also a 2022 FNP START laureate and one of the selected participants in the 2024 Lindau Nobel Laureate Meeting. On the second occasion, I answered “10 Questions” for the Women in Research Blog.
Research directions
Understanding machine learning
Why overparametrized models generalize so well? Is generalization related to the flatness of the training loss minimum? What is the reason for double descent? What data features are learned by machines?
Machine learning for sciences
How to boost quantum experiments with deep learning? How to automatically detect local and global order parameters of quantum phase transitions? Can we learn new physics from trained neural networks?
Ultracold molecules
What happens when we have both magnetic and electric excitations which are additionally coupled? Can we go beyond diatomic molecules with diagonal Franck-Condon factors with laser cooling?
Quantum simulations
What novel phases of matter can we design? How to understand high-temperature superconductivity with quantum simulators?
Featured publications
O. Simard*, A. Dawid*, J. Tindall, M. Ferrero, A. M. Sengupta, A. Georges. Learning interactions between Rydberg atoms. PRX Quantum 6, 030324 (2025).
A. Vysogorets, A. Dawid, J. Kempe. Deconstructing the Goldilocks zone of neural network initialization. ICML ’24, arXiv:2402.03579.
See the summary on X!
A. Dawid, N. Bigagli, D. W. Savin, S. Will. Automated graph-based detection of quantum control schemes: Application to molecular laser cooling. Phys. Rev. Research 7, 013135 (2025).
See the summary on X!
News
Aug 28, 2025
I’m proud to announce that Bartosz Kreft has just defended his Master’s thesis, in which he successfully tested the scalability limits of graph neural networks in the Hamiltonian learning problem (it seems there are none…). What’s even better is that he’ll continue his PhD studies in our group and push forward our efforts in scalable machine learning for quantum physics! Bartek, welcome aboard!
Feb 15, 2025
I’m extremely happy to announce that the first PhD student has just started his studies under my supervision. Björn van Zwol will further develop interpretable neural networks for quantum physics, including our TetrisCNN. I’m so looking forward to working with you 🙂