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Looking elsewhere: improving variational Monte Carlo gradients by importance sampling

Neural-network quantum states (NQSs) offer a powerful and expressive ansatz for representing quantum many-body wave functions. However, their training via Variational Monte Carlo (VMC) methods remains challenging. It is well known that some scenarios—such as sharply peaked wave functions emerging in...

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Hlavní autoři: Antoine Misery, Luca Gravina, Alessandro Santini, Filippo Vicentini
Médium: Artigo
Jazyk:Inglês
Vydáno: IOP Publishing 2026-01-01
Edice:Machine Learning: Science and Technology
Témata:
variational Monte Carlo
neural network quantum states
importance sampling
ab-initio quantum chemistry
quantum physics
variance reduction
On-line přístup:https://doi.org/10.1088/2632-2153/ae387f
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