Approximating Ground States by Neural Network Quantum States
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 Hamilton...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/21/1/82 |
| _version_ | 1828391872567967744 |
|---|---|
| author | Ying Yang Chengyang Zhang Huaixin Cao |
| author_facet | Ying Yang Chengyang Zhang Huaixin Cao |
| author_sort | Ying Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:10:01Z |
| format | Article |
| id | doaj.art-a535037f2ac24f57aaed1e8d8b452b80 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-12-10T07:10:01Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a535037f2ac24f57aaed1e8d8b452b802022-12-22T01:58:04ZengMDPI AGEntropy1099-43002019-01-012118210.3390/e21010082e21010082Approximating Ground States by Neural Network Quantum StatesYing Yang0Chengyang Zhang1Huaixin Cao2School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, ChinaMotivated 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.http://www.mdpi.com/1099-4300/21/1/82approximationground stateneural network quantum state |
| spellingShingle | Ying Yang Chengyang Zhang Huaixin Cao Approximating Ground States by Neural Network Quantum States Entropy approximation ground state neural network quantum state |
| title | Approximating Ground States by Neural Network Quantum States |
| title_full | Approximating Ground States by Neural Network Quantum States |
| title_fullStr | Approximating Ground States by Neural Network Quantum States |
| title_full_unstemmed | Approximating Ground States by Neural Network Quantum States |
| title_short | Approximating Ground States by Neural Network Quantum States |
| title_sort | approximating ground states by neural network quantum states |
| topic | approximation ground state neural network quantum state |
| url | http://www.mdpi.com/1099-4300/21/1/82 |
| work_keys_str_mv | AT yingyang approximatinggroundstatesbyneuralnetworkquantumstates AT chengyangzhang approximatinggroundstatesbyneuralnetworkquantumstates AT huaixincao approximatinggroundstatesbyneuralnetworkquantumstates |