Path optimization with machine learning reproduces analytic results in the 1D lattice Thirring model, and dropping the Jacobian from the learning step still works.
Fighting the sign problem in a chiral random matrix model with contour deformations
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abstract
We studied integration contour deformations in the chiral random matrix theory of Stephanov with the goal of alleviating the finite-density sign problem. We considered simple ans\"atze for the deformed integration contours, and optimized their parameters. We find that optimization of a single parameter manages to considerably improve on the severity of the sign problem. We show numerical evidence that the improvement achieved is exponential in the degrees of freedom of the system, i.e., the size of the random matrix. We also compare the optimization method with contour deformations coming from the holomorphic flow equations.
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Path optimization method for the sign problem caused by fermion determinant
Path optimization with machine learning reproduces analytic results in the 1D lattice Thirring model, and dropping the Jacobian from the learning step still works.