Adaptively tuned overdispersed importance sampling, q_alpha proportional to |psi|^alpha, cuts the Monte Carlo sample count needed to converge neural quantum states, especially for peaked molecular wavefunctions.
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Looking elsewhere: improving variational Monte Carlo gradients by importance sampling
Adaptively tuned overdispersed importance sampling, q_alpha proportional to |psi|^alpha, cuts the Monte Carlo sample count needed to converge neural quantum states, especially for peaked molecular wavefunctions.