A stochastic MCMC sampling method with umbrella sampling provides unbiased loop corrections to belief propagation for exact factorization-based tensor network contraction on loopy graphs with symmetric potentials.
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4 Pith papers cite this work. Polarity classification is still indexing.
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H-PIMC and M-PIMC split the potential into harmonic and anharmonic pieces, sample the harmonic piece exactly, and report 6-16x higher acceptance and 7-30x lower autocorrelation for moderately anharmonic systems at βℏω=16, with further gains when combined with the worm algorithm.
Extrapolated ground-state energy density reaches -0.669441857(7) and sublattice magnetization 0.307447(2) for the 2D S=1/2 Heisenberg antiferromagnet, with finite-size corrections matching chiral perturbation theory.
citing papers explorer
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Stochastic Loop Corrections to Belief Propagation for Tensor Network Contraction
A stochastic MCMC sampling method with umbrella sampling provides unbiased loop corrections to belief propagation for exact factorization-based tensor network contraction on loopy graphs with symmetric potentials.
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Combining Harmonic Sampling with the Worm Algorithm to Improve the Efficiency of Path Integral Monte Carlo
H-PIMC and M-PIMC split the potential into harmonic and anharmonic pieces, sample the harmonic piece exactly, and report 6-16x higher acceptance and 7-30x lower autocorrelation for moderately anharmonic systems at βℏω=16, with further gains when combined with the worm algorithm.
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High-precision ground state parameters of the two-dimensional spin-1/2 Heisenberg model on the square lattice
Extrapolated ground-state energy density reaches -0.669441857(7) and sublattice magnetization 0.307447(2) for the 2D S=1/2 Heisenberg antiferromagnet, with finite-size corrections matching chiral perturbation theory.
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