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
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
citing papers explorer
-
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