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Title: A Future with Quantum Machine Learning

Abstract

Could combining quantum computing and machine learning with Moore's law produce a true “rebooted computer”? Furthermore, this article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.

Authors:
 [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1459995
Report Number(s):
SAND-2018-7015J
Journal ID: ISSN 0018-9162; 665271
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Computer
Additional Journal Information:
Journal Volume: 51; Journal Issue: 2; Journal ID: ISSN 0018-9162
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; rebooting computing; machine learning; quantum computing; Moore’s law; energy efficiency; green computing; history of computing; future of computing

Citation Formats

DeBenedictis, Erik P. A Future with Quantum Machine Learning. United States: N. p., 2018. Web. doi:10.1109/mc.2018.1451646.
DeBenedictis, Erik P. A Future with Quantum Machine Learning. United States. doi:10.1109/mc.2018.1451646.
DeBenedictis, Erik P. Fri . "A Future with Quantum Machine Learning". United States. doi:10.1109/mc.2018.1451646. https://www.osti.gov/servlets/purl/1459995.
@article{osti_1459995,
title = {A Future with Quantum Machine Learning},
author = {DeBenedictis, Erik P.},
abstractNote = {Could combining quantum computing and machine learning with Moore's law produce a true “rebooted computer”? Furthermore, this article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.},
doi = {10.1109/mc.2018.1451646},
journal = {Computer},
number = 2,
volume = 51,
place = {United States},
year = {2018},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: Optimization involves finding the lowest point on a potential energy curve (blue), which is Death Valley even though most water flows to the oceans. Classical optimization (orange) works like raindrops flowing downhill, but simulated annealing allows limited uphill movement (purple). However, quantum computer optimization can use a quantummore » physics« less

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