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Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation

NeurIPS 2023 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Stochastic gradient MCMC · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace QVMC with WQMCQVMC (Psiformer) · Berelative energy error: 1.50e-5 -> 3.79e-6Helped
replace QVMC with W(FR)QMCQVMC (Psiformer) · Berelative energy error: 1.50e-5 -> 1.04e-6Helped
replace QVMC with WQMCQVMC (Psiformer) · Brelative energy error: 9.01e-6 -> 9.58e-6No clear effect
replace QVMC with W(FR)QMCQVMC (Psiformer) · Brelative energy error: 9.01e-6 -> 2.69e-6Helped
replace QVMC with WQMCQVMC (Psiformer) · Li2relative energy error: 4.43e-5 -> 3.71e-5Helped
replace QVMC with W(FR)QMCQVMC (Psiformer) · Li2relative energy error: 4.43e-5 -> 3.66e-5Helped
replace QVMC with WQMCQVMC (Psiformer) · H10relative energy error: 4.24e-4 -> 3.90e-4Helped
replace QVMC with W(FR)QMCQVMC (Psiformer) · H10relative energy error: 4.24e-4 -> 3.88e-4Helped
replace QVMC with WQMCQVMC (Psiformer) · Beenergy variance: 5.53e-3 -> 1.08e-3Helped
replace QVMC with W(FR)QMCQVMC (Psiformer) · Benergy variance: 1.96e-2 -> 1.19e-2Helped
replace QVMC with WQMCQVMC (Psiformer) · Li2energy variance: 7.21e-3 -> 5.76e-4Helped
replace QVMC with WQMCQVMC (Psiformer) · H10energy variance: 1.89e-3 -> 2.30e-4Helped

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