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Stochastic Estimation with $Z_2$ Noise
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abstract
We introduce a $Z_2$ noise for the stochastic estimation of matrix inversion and discuss its superiority over other noises including the Gaussian noise. This algorithm is applied to the calculation of quark loops in lattice quantum chromodynamics that involves diagonal and off-diagonal traces of the inverse matrix. We will point out its usefulness in its applications to estimating determinants, eigenvalues, and eigenvectors, as well as its limitations based on the structure of the inverse matrix.
Forward citations
Cited by 3 Pith papers
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Using Tr M^-1 as both an input and a feature, a bias-corrected ML model predicts Tr M^-2..-4 and reproduces chiral-condensate cumulants with ~1% labeled data at ~26% of the original cost.
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