Second-order optimization strategies for neural network quantum states.
Philos Trans A Math Phys Eng Sci
; 382(2275): 20240057, 2024 Jul 23.
Article
in En
| MEDLINE
| ID: mdl-38910393
ABSTRACT
The Variational Monte Carlo (VMC) method has recently seen important advances through the use of neural network quantum states. While more and more sophisticated ansatze have been designed to tackle a wide variety of quantum many-body problems, modest progress has been made on the associated optimization algorithms. In this work, we revisit the Kronecker-Factored Approximate Curvature (KFAC), an optimizer that has been used extensively in a variety of simulations. We suggest improvements in the scaling and the direction of this optimizer and find that they substantially increase its performance at a negligible additional cost. We also reformulate the VMC approach in a game theory framework, to propose a novel optimizer based on decision geometry. We find that on a practical test case for continuous systems, this new optimizer consistently outperforms any of the KFAC improvements in terms of stability, accuracy and speed of convergence. Beyond VMC, the versatility of this approach suggests that decision geometry could provide a solid foundation for accelerating a broad class of machine learning algorithms. This article is part of the theme issue 'The liminal position of Nuclear Physics from hadrons to neutron stars'.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Philos Trans A Math Phys Eng Sci
Journal subject:
BIOFISICA
/
ENGENHARIA BIOMEDICA
Year:
2024
Document type:
Article
Affiliation country:
Canada
Country of publication:
United kingdom