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Title: Quantum neural networks to simulate many-body quantum systems

Abstract

We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.

Authors:
 [1];  [2];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Silesia, Katowice (Poland); Jagiellonian Univ., Krakow (Poland)
  2. Jagiellonian Univ., Krakow (Poland)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
Domestic Funding; USDOE
OSTI Identifier:
1525838
Report Number(s):
LA-UR-18-23838
Journal ID: ISSN 2469-9950; PRBMDO
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
Journal Volume: 98; Journal Issue: 18; Journal ID: ISSN 2469-9950
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
Quantum Computing; Machine learning; D-Wave

Citation Formats

Gardas, Bartłomiej, Rams, Marek M., and Dziarmaga, Jacek. Quantum neural networks to simulate many-body quantum systems. United States: N. p., 2018. Web. doi:10.1103/PhysRevB.98.184304.
Gardas, Bartłomiej, Rams, Marek M., & Dziarmaga, Jacek. Quantum neural networks to simulate many-body quantum systems. United States. doi:10.1103/PhysRevB.98.184304.
Gardas, Bartłomiej, Rams, Marek M., and Dziarmaga, Jacek. Mon . "Quantum neural networks to simulate many-body quantum systems". United States. doi:10.1103/PhysRevB.98.184304.
@article{osti_1525838,
title = {Quantum neural networks to simulate many-body quantum systems},
author = {Gardas, Bartłomiej and Rams, Marek M. and Dziarmaga, Jacek},
abstractNote = {We conduct experimental simulations of many-body quantum systems using a hybrid classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-wave quantum sampler to find the ground-state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle nontrivial problems concerning physics of many-body quantum systems.},
doi = {10.1103/PhysRevB.98.184304},
journal = {Physical Review B},
issn = {2469-9950},
number = 18,
volume = 98,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 26, 2019
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