Approximating Ground States by Neural Network Quantum States

Ying Yang; Chengyang Zhang; Huaixin Cao

doi:10.3390/e21010082

Approximating Ground States by Neural Network Quantum States

Entropy (Basel). 2019 Jan 17;21(1):82. doi: 10.3390/e21010082.

Authors

Ying Yang^{1

2}, Chengyang Zhang¹, Huaixin Cao¹

Affiliations

¹ School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.
² School of Mathematics and Information Technology, Yuncheng University, Yuncheng 044000, China.

Abstract

Motivated by the Carleo's work (Science, 2017, 355: 602), we focus on finding the neural network quantum statesapproximation of the unknown ground state of a given Hamiltonian H in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples.

Keywords: approximation; ground state; neural network quantum state.