Variational Monte Carlo
19 papers with code • 0 benchmarks • 0 datasets
Variational methods for quantum physics
Benchmarks
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Most implemented papers
Towards a Foundation Model for Neural Network Wavefunctions
Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model.
Spectral Inference Networks: Unifying Deep and Spectral Learning
deepmind/spectral_inference_networks
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ICLR 2019
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
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.
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
ywolfeee/lapjax
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Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
Natural Quantum Monte Carlo Computation of Excited States
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states.
Transferable Neural Wavefunctions for Solids
To mitigate this problem, recent research has proposed optimizing a single neural network across multiple systems, reducing the cost per system.
Streaming Variational Monte Carlo
catniplab/svmc
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Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series.
Natural evolution strategies and variational Monte Carlo
Ericolony/QAOA
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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
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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.
