Paired autoregressive transformers with disjoint sign-sector support generate zero-mean control variates that cut the standard error of the average sign by up to 10x and the energy estimator by 3-5x in small-N SSE simulations of the triangular-lattice Heisenberg antiferromagnet.
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Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
Paired autoregressive transformers with disjoint sign-sector support generate zero-mean control variates that cut the standard error of the average sign by up to 10x and the energy estimator by 3-5x in small-N SSE simulations of the triangular-lattice Heisenberg antiferromagnet.