Berlin 2024 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 5: Machine Learning in Dynamics and Statistical Physics I

DY 5.8: Talk

Monday, March 18, 2024, 11:30–11:45, BH-N 243

Quantum Phase Transitions with Neural Network Quantum States and a Lee-Yang Method — •Pascal M. Vecsei, Jose L. Lado, and Christian Flindt — Aalto University, Otakaari 1, 02150 Espoo, Finland

Predicting the phase diagram of interacting quantum many-body systems is a central problem in condensed matter physics and related fields. A variety of quantum many-body systems, ranging from unconventional superconductors to spin liquids, exhibit complex competing phases whose theoretical description has been the focus of intense efforts. Here, we show that neural network quantum states can be combined with a Lee-Yang method to investigate quantum phase transitions and predict the critical points of strongly correlated spin lattices [1,2]. Specifically, we implement our approach for quantum phase transitions in the transverse-field Ising model on different lattice geometries in one, two, and three dimensions. We show that the Lee-Yang method combined with neural network quantum states yields predictions of the critical field, which are consistent with large-scale quantum many-body methods. As such, our results provide a starting point for determining the phase diagram of more complex quantum many-body systems, including frustrated Heisenberg models.

[1] Pascal M. Vecsei, J. L. Lado, C. Flindt, Phys. Rev. B 106, 054402 (2022)

[2] Pascal M. Vecsei, C. Flindt, J. L. Lado, Phys. Rev. Research 5, 033116 (2023)

Keywords: Lee-Yang Zeros; Neural Network Quantum States; Transverse Field Ising Model