Dresden 2026 – scientific programme
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DY: Fachverband Dynamik und Statistische Physik
DY 14: Machine Learning in Dynamics and Statistical Physics II
DY 14.12: Talk
Monday, March 9, 2026, 18:00–18:15, HÜL/S186
Solving Classical and Quantum spin glasses with Deep Boltzman Quantum States — Luca Leone1, •Arka Dutta1, Markus Heyl1, Enrico Prati2, and Pietro Torta2 — 1Theoretical Physics III, Center for Electronic Correlations and Magnetism, Institute of Physics, University of Augsburg, Augsburg, Germany. — 2Department of Physics, University of Milan, Milan, Italy.
Variational neural network models achieved remarkable success in preparing the ground state of quantum many-body systems. However, addressing classical and quantum spin glasses remains challenging, as exponential growth of deep local energy minima due to disorder and energy frustration hinder conventional Monte Carlo methods. To bridge this gap, we introduce Deep Boltzmann Quantum States, a class of neural quantum states inspired by deep Boltzmann machines, trained by devising Neural Quantum Annealing, an algorithm incorporating the principles of quantum annealing. It solves large-scale classical and quantum spin glasses, matching the exact solution or the best available estimate for several instances of Ising spin-glass models with infinite-range interactions and hundreds of spins.
Keywords: Neural quantum states; Spin glasses; Quantum annealing; Variational methods