VMC local energy and gradient estimators are generically heavy-tailed for common ansatze due to nodal sets, but a new clipped variant converges in the low-moment regime.
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Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization
VMC local energy and gradient estimators are generically heavy-tailed for common ansatze due to nodal sets, but a new clipped variant converges in the low-moment regime.
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Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac