# Variational Monte Carlo

13 papers with code • 0 benchmarks • 0 datasets

Variational methods for quantum physics

## Benchmarks

These leaderboards are used to track progress in Variational Monte Carlo
## Most implemented papers

# Solving Statistical Mechanics Using Variational Autoregressive Networks

We propose a general framework for solving statistical mechanics of systems with finite size.

# Deep autoregressive models for the efficient variational simulation of many-body quantum systems

Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states.

# Spectral Inference Networks: Unifying Deep and Spectral Learning

We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.

# Natural evolution strategies and variational Monte Carlo

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.

# Convergence to the fixed-node limit in deep variational Monte Carlo

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.

# Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science.

# Continuous-variable neural-network quantum states and the quantum rotor model

We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization.

# Autoregressive neural-network wavefunctions for ab initio quantum chemistry

In recent years, neural network quantum states (NNQS) have emerged as powerful tools for the study of quantum many-body systems.

# Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions

Solving the Schr\"odinger equation is key to many quantum mechanical properties.

# Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

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.