.png){kind=icon}
.png){kind=icon}
Return
Recurrent neural network wave functions
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevResearch.2.023358.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable likelihood estimate with stable training paradigms, a combination that has precipitated many spectacular advances in natural language processing and neural machine translation. This architecture also makes a good candidate for a variational wave function, where the RNN parameters are tuned to learn the approximate ground state of a quantum Hamiltonian. In this paper, we demonstrate the ability of RNNs to represent several many-body wave functions, optimizing the variational parameters using a stochastic approach. Among other attractive features of these variational wave functions, their autoregressive nature allows for the efficient calculation of physical estimators by providing independent samples. We demonstrate the effectiveness of RNN wave functions by calculating ground-state energies, correlation functions, and entanglement entropies for several quantum spin models of interest to condensed-matter physicists in one and two spatial dimensions.
Keywords:
SCHRODINGER-EQUATION
MONTE-CARLO
QUANTUM
PRINCIPLE
STATES
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
7.6K
Citations:
2.7W