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Path optimization method for the sign problem caused by fermion determinant
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Path optimization method for the sign problem caused by fermion determinant
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The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation.
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Cited by 1 Pith paper
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Path optimization method for the sign problem: Insights from random matrix models
Path optimization improves the average phase factor in the Stephanov model at high chemical potential but not at low chemical potential or in the chiral random matrix model, pointing to the global sign problem as the ...
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