Variational Monte Carlo

13 papers with code • 0 benchmarks • 0 datasets

Variational methods for quantum physics

Most implemented papers

Solving Statistical Mechanics Using Variational Autoregressive Networks

wdphy16/stat-mech-van 27 Sep 2018

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

HUJI-Deep/FlowKet 11 Feb 2019

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

deepmind/spectral_inference_networks ICLR 2019

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

Natural evolution strategies and variational Monte Carlo

Ericolony/QAOA 9 May 2020

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

deepqmc/deepqmc 11 Oct 2020

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

wdphy16/neural-cluster-update 12 May 2021

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

shravanvn/cnqs 15 Jul 2021

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

tomdbar/naqs-for-quantum-chemistry 26 Sep 2021

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

n-gao/pesnet ICLR 2022

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

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

jeffminlin/vmcnet 7 Dec 2021

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.