Regensburg 2025 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 28: Poster: Machine Learning, Data Science

DY 28.1: Poster

Wednesday, March 19, 2025, 15:00–18:00, P4

Thermal Neural Quantum States — •Atiye Abedinnia and Annabelle Bohrdt — Institute of theoretical physics, University of Regensburg

Finite-temperature effects play an important role in the design and optimization of quantum devices, as decoherence and noise often originate from thermal fluctuations. At finite temperatures, quantum systems are described by a statistical ensemble of states rather than a single pure state. Simulating such thermal states requires constructing the thermal density matrix, which suffers from significant computational challenges due to the exponential growth of the Hilbert space with system size. So far, purification methods(thermofield) in the context of MPS and Minimally Entangled Typical Thermal States (METTS) approach have been developed in the context of tensor networks. In this work, we propose using neural quantum states (NQS), leveraging the expressivity and scalability of transformer-based architectures to address the challenges of thermal equilibrium density matrix representation.

Keywords: Neural Quantum States; many-body physics; density matrix