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Title: Quantum Computing for Quantum Simulation for NNSA Mission

Abstract

Beginning with Feynman in 1982, it was proposed that highly controllable quantum devices, later to become known as quantum computers, would be especially good at the simulation of other quantum systems. Around the turn of the 21st century, this notion was later formalized (by Lloyd and coworkers at MIT and Laflamme and coworkers at Los Alamos) to show how in particular instances, we expect an exponential speedup in the solution of the Schroedinger equation for chemical systems. Subsequently, quantum-accelerated quantum simulation for chemistry techniques have been developed by IBM, Google, Harvard, etc. This effort has led to the OpenFermion (openfermion.org, OF) community, an open source effort for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, the current version of OF features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. In October, 2017, the cover of Chemical and Engineering News posited “Quantum computers: Chemistry could be the machine’s killer app” for first principles understanding of biological systems and complex materials. Prof. Aspuru-Guzik (Harvard) predicts “I think that before 2035 QC will be beating classical computers at chemistry.” The algorithms at the heart of quantum chemistry codes (eigensolvers, fastmore » fourier transforms) are the computation bottleneck.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1496743
Report Number(s):
LA-UR-19-21583
DOE Contract Number:  
89233218CNA000001
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Kress, Joel David, Zhu, Jianxin, and Barros, Kipton Marcos. Quantum Computing for Quantum Simulation for NNSA Mission. United States: N. p., 2019. Web. doi:10.2172/1496743.
Kress, Joel David, Zhu, Jianxin, & Barros, Kipton Marcos. Quantum Computing for Quantum Simulation for NNSA Mission. United States. doi:10.2172/1496743.
Kress, Joel David, Zhu, Jianxin, and Barros, Kipton Marcos. Mon . "Quantum Computing for Quantum Simulation for NNSA Mission". United States. doi:10.2172/1496743. https://www.osti.gov/servlets/purl/1496743.
@article{osti_1496743,
title = {Quantum Computing for Quantum Simulation for NNSA Mission},
author = {Kress, Joel David and Zhu, Jianxin and Barros, Kipton Marcos},
abstractNote = {Beginning with Feynman in 1982, it was proposed that highly controllable quantum devices, later to become known as quantum computers, would be especially good at the simulation of other quantum systems. Around the turn of the 21st century, this notion was later formalized (by Lloyd and coworkers at MIT and Laflamme and coworkers at Los Alamos) to show how in particular instances, we expect an exponential speedup in the solution of the Schroedinger equation for chemical systems. Subsequently, quantum-accelerated quantum simulation for chemistry techniques have been developed by IBM, Google, Harvard, etc. This effort has led to the OpenFermion (openfermion.org, OF) community, an open source effort for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, the current version of OF features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. In October, 2017, the cover of Chemical and Engineering News posited “Quantum computers: Chemistry could be the machine’s killer app” for first principles understanding of biological systems and complex materials. Prof. Aspuru-Guzik (Harvard) predicts “I think that before 2035 QC will be beating classical computers at chemistry.” The algorithms at the heart of quantum chemistry codes (eigensolvers, fast fourier transforms) are the computation bottleneck.},
doi = {10.2172/1496743},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {2}
}

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