Work

Deep learning-enhanced variational Monte Carlo method for quantum many-body physics

Year: 2020

Type: article

Abstract: The authors construct and develop an optimization scheme to train a deep convolutional neural network to represent many-body wave function. The paper explores its performance by applying the network to find the ground state of an SU(N) spin-chain Hamiltonian using variational quantum Monte Carlo.

Source: Physical Review Research

DOI: 10.1103/physrevresearch.2.012039

Authors: Li Yang, Zhaoqi Leng, Guangyuan Yu, Ankit Patel, Wen-Jun Hu, Han Pu

Institutions: Rice University, Princeton University, Center for Theoretical Biological Physics, Baylor College of Medicine

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: National Science Foundation, U.S. Department of Energy, Welch Foundation