Rigorous bounds prove that the number of complex exponentials needed for non-Markovian bath correlations is bounded independently of T for many spectral densities, with mild T-dependence only for strong singularities.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.
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Provably Efficient Long-Time Exponential Decompositions of Non-Markovian Gaussian Baths
Rigorous bounds prove that the number of complex exponentials needed for non-Markovian bath correlations is bounded independently of T for many spectral densities, with mild T-dependence only for strong singularities.
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Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods
Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.