Variance extrapolation method for neural-network variational Monte Carlo

Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate ab initio calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, whic...

Full description

Saved in:
Bibliographic Details
Published in:Machine learning: science and technology 2024-03, Vol.5 (1), p.15016
Main Authors: Fu, Weizhong, Ren, Weiluo, Chen, Ji
Format: Article
Language:English
Subjects:
electronic structure
Extrapolation
neural network quantum state
Neural networks
Training
variational Monte Carlo
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate ab initio calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, which may have a counterproductive effect on the accuracy of calculation. In this work, we propose to make use of the training data to perform empirical variance extrapolation when using neural-network ansatz in variational Monte Carlo. We show that this approach can speed up the convergence and surpass the ansatz limitation to obtain an improved estimation of the energy. Moreover, variance extrapolation greatly enhances the error cancellation capability, resulting in significantly improved relative energy outcomes, which are the keys to chemistry and physics problems.
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ad1f75