Variational Monte Carlo
13 papers with code • 0 benchmarks • 0 datasets
Variational methods for quantum physics
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Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states.
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.
Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice.
Efficient sampling of complex high-dimensional probability distributions is a central task in computational science.
We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization.
In recent years, neural network quantum states (NNQS) have emerged as powerful tools for the study of quantum many-body systems.
We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks.