Ground state search by local and sequential updates of neural network quantum states

被引:9
作者
Zhang, Wenxuan [1 ]
Xu, Xiansong [1 ]
Wu, Zheyu [1 ]
Balachandran, Vinitha [1 ]
Poletti, Dario [1 ,2 ,3 ,4 ]
机构
[1] Singapore Univ Technol & Design, Sci Math & Technol Cluster, 8 Somapah Rd, Singapore 487372, Singapore
[2] Singapore Univ Technol & Design, Engn Prod Dev Pillar, 8 Somapah Rd, Singapore 487372, Singapore
[3] Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy
[4] Natl Univ Singapore, Ctr Quantum Technol, Singapore 117543, Singapore
关键词
MONTE-CARLO-SIMULATION; DYNAMICS;
D O I
10.1103/PhysRevB.107.165149
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Neural network quantum states are a promising tool to analyze complex quantum systems given their representative power. It can however be difficult to optimize efficiently and effectively the parameters of this type of ansatz. Here we propose a local optimization procedure which, when integrated with stochastic reconfiguration, outperforms previously used global optimization approaches. Specifically, we analyze both the ground state energy and the correlations for the nonintegrable tilted Ising model with restricted Boltzmann machines. We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated. To show the generality of the approach we apply it to both 1D and 2D nonintegrable spin systems.
引用
收藏
页数:6
相关论文
共 73 条
[1]   Learning the dynamics of open quantum systems from their steady states [J].
Bairey, Eyal ;
Guo, Chu ;
Poletti, Dario ;
Lindner, Netanel H. ;
Arad, Itai .
NEW JOURNAL OF PHYSICS, 2020, 22 (03)
[2]   Modelling non-markovian quantum processes with recurrent neural networks [J].
Banchi, Leonardo ;
Grant, Edward ;
Rocchetto, Andrea ;
Severini, Simone .
NEW JOURNAL OF PHYSICS, 2018, 20
[3]   Applications of neural networks to the simulation of dynamics of open quantum systems [J].
Bandyopadhyay, Sayantan ;
Huang, Zhongkai ;
Sun, Kewei ;
Zhao, Yang .
CHEMICAL PHYSICS, 2018, 515 :272-278
[4]   Training deep quantum neural networks [J].
Beer, Kerstin ;
Bondarenko, Dmytro ;
Farrelly, Terry ;
Osborne, Tobias J. ;
Salzmann, Robert ;
Scheiermann, Daniel ;
Wolf, Ramona .
NATURE COMMUNICATIONS, 2020, 11 (01)
[5]   Quantum machine learning [J].
Biamonte, Jacob ;
Wittek, Peter ;
Pancotti, Nicola ;
Rebentrost, Patrick ;
Wiebe, Nathan ;
Lloyd, Seth .
NATURE, 2017, 549 (7671) :195-202
[6]   Approximating quantum many-body wave functions using artificial neural networks [J].
Cai, Zi ;
Liu, Jinguo .
PHYSICAL REVIEW B, 2018, 97 (03)
[7]   Machine learning and the physical sciences [J].
Carleo, Giuseppe ;
Cirac, Ignacio ;
Cranmer, Kyle ;
Daudet, Laurent ;
Schuld, Maria ;
Tishby, Naftali ;
Vogt-Maranto, Leslie ;
Zdeborova, Lenka .
REVIEWS OF MODERN PHYSICS, 2019, 91 (04)
[8]   Solving the quantum many-body problem with artificial neural networks [J].
Carleo, Giuseppe ;
Troyer, Matthias .
SCIENCE, 2017, 355 (6325) :602-605
[9]   How To Use Neural Networks To Investigate Quantum Many-Body Physics [J].
Carrasquilla, Juan ;
Torlai, Giacomo .
PRX QUANTUM, 2021, 2 (04)
[10]  
Carrasquilla J, 2017, NAT PHYS, V13, P431, DOI [10.1038/nphys4035, 10.1038/NPHYS4035]