Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Several strategies have been recently proposed in order to improve Monte Carlo sampling
efficiency using machine learning tools. Here, we challenge these methods by considering a …

Unraveling the connections between microscopic structure, emergent physical properties,
and slow dynamics has long been a challenge when studying the glass transition. The …

Efficient sampling and approximation of Boltzmann distributions involving large sets of
binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent …

Deep neural networks have been used to solve Ising models, including autoregressive
neural networks, convolutional neural networks, recurrent neural networks, and graph …

We study the triangular plaquette model (TPM), also known as the Newman-Moore model, in
the presence of a transverse magnetic field on a lattice with periodic boundaries in both …

Unravelling the connections between microscopic structure, emergent physical properties
and slow dynamics has long been a challenge when studying the glass transition. The …

Combinatorial optimization problems can be solved by heuristic algorithms such as
simulated annealing (SA) which aims to find the optimal solution within a large search space …

Abstract Autoregressive Neural Networks (ARNNs) have shown exceptional results in
generation tasks across image, language, and scientific domains. Despite their success …

Abstract Neural Quantum States (NQS) have demonstrated significant potential in
approximating ground states of many-body quantum systems, though their performance can …