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TT: Fachverband Tiefe Temperaturen
TT 2: Focus Session: Artificial Intelligence in Condensed Matter Physics I (joint session TT/DY)
TT 2.5: Invited Talk
Monday, March 18, 2024, 11:45–12:15, H 0104
Neural quantum states for strongly correlated systems: learning from data and Hamiltonians — •Annabelle Bohrdt1, Hannah Lange2, Schuyler Moss3, Fabian Döschl2, Felix Palm2, Giulia Semeghini4, Mikhail Lukin4, Sepehr Ebadi4, Tout Wang4, Fabian Grusdt2, Juan Carrasquilla5, and Roger Melko3 — 1Universität Regensburg — 2LMU München — 3UWaterloo — 4Harvard University — 5Vector Institute
Neural quantum states have emerged as a new tool to efficiently represent quantum many-body states with two main use cases: 1.) efficiently reconstruct a quantum state by training on measured data. For states with a non-trivial sign structure, measurements in many different basis configurations are necessary. I will present an active learning scheme which adaptively chooses the next measurement basis in order to maximize the information gain. 2.) The second main application of neural quantum states is to apply variational Monte Carlo to find e.g. the ground state of a system. I will present some of our recent results on ground states of strongly correlated systems, such as t-J type systems and fractional quantum Hall states. Finally, we combine both approaches: by first training on experimental data from a Rydberg atom tweezer array, we initialize the neural quantum state closer to the ground state. By then switching to variational Monte Carlo to minimize the energy in the second stage of training, we find a speedup in convergence. This showcases how limited datasets from experiments can be combined with numerical methods in a hybrid approach to yield more accurate results than either could provide on their own.
Keywords: Neural quantum states; interacting quantum many-body systems; hybrid learning; active learning