Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations

This collection contains the pre-trained RBM models and figures for the manuscript: "Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations". In the associated paper, we investigate the sampling advantage of stochastic Ising Machines for Neural Quantum States. The pre-trained RBM models are obtained using UltraFast.jl which is also used to generate samples with Metropolis-Hastings sampling. In addition, the pre-trained RBM models are translated to an Ising Model and sampled using chromatic Gibbs sampling. For the samples obtained with both Metropolis-Hastings and chromatic Gibbs sampling, the autocorrelation time of the variational energy is calculated.