Efficient Solutions of Fermionic Systems using Artificial Neural Networks

Abstract

We discuss differences and similarities between variational Monte Carlo approaches that use conventional and artificial neural network parameterizations of the ground-state wave function for systems of fermions. We focus on a relatively shallow neural-network architectures, the so called restricted Boltzmann machine, and discuss unsupervised learning algorithms that are suitable to model complicated many-body correlations. We analyze the strengths and weaknesses of conventional and neural-network wave functions by solving various circular quantum-dots systems. Results for up to 90 electrons are presented and particular emphasis is placed on how to efficiently implement these methods on homogeneous and heterogeneous high-performance computing facilities.

References in corpus (7)

• Jastrow correlation factor for atoms, molecules, and solids
• Machine Learning in Nuclear Physics
• Discovering Quantum Phase Transitions with Fermionic Neural Networks
• Incipient Wigner Localization in Circular Quantum Dots
• On the Sign Problem of the Fermionic Shadow Wave Function
• Solving the nuclear pairing model with neural network quantum states
• Dilute neutron star matter from neural-network quantum states

Cited by in corpus (2)

• Neural network expansion of Euclidean path integrals and its application to interacting scalar fields
• Approximating Grassmann valued path integrals with radial basis function neural networks