Abstrakt:
Quantum many-body physics is rich in challenges, such as understanding and designing novel phases of matter, finding ground states of large systems with complex interactions, or improving quantum simulating and computing platforms. A new data-driven paradigm of machine learning is rising, promising breakthroughs in every listed domain. While delivering impressive results in real-world applications, the black-box construction of neural networks often hinders their effectiveness in scientific research. As a result, within my research, I focus on interpretable and reliable machine learning to help in scientific discovery. In particular, I will present our efforts in interpretable machine learning of phases of matter and their order parameters using custom-made methods. To show another side of automated discovery, I will also discuss a graph search algorithm that we designed to locate laser cooling schemes out of spectroscopic data. I will conclude by discussing the exciting open problems of the field.
Biogram:
Anna Dawid-Łękowska is a research fellow at the Center of Computational Quantum Physics of the Flatiron Institute in New York, happily playing with interpretable machine learning for science and ultracold molecules for quantum simulations. She defended her joint PhD degree in physics and photonics in September 2022 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, she did her MSc in quantum chemistry and BSc in Biotechnology at the University of Warsaw.
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