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Title: Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Abstract

A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Jonathan Schrock; Alex McCaskey; Kathleen Hamilton; Travis Humble; Neena Imam
OSTI Identifier:
1394279
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Entropy
Additional Journal Information:
Journal Volume: 19; Journal Issue: 9; Journal ID: ISSN 1099-4300
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Imam, Neena, Humble, Travis S., McCaskey, Alex, Schrock, Jonathan, and Hamilton, Kathleen E. Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization. United States: N. p., 2017. Web. doi:10.3390/e19090500.
Imam, Neena, Humble, Travis S., McCaskey, Alex, Schrock, Jonathan, & Hamilton, Kathleen E. Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization. United States. doi:10.3390/e19090500.
Imam, Neena, Humble, Travis S., McCaskey, Alex, Schrock, Jonathan, and Hamilton, Kathleen E. Fri . "Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization". United States. doi:10.3390/e19090500.
@article{osti_1394279,
title = {Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization},
author = {Imam, Neena and Humble, Travis S. and McCaskey, Alex and Schrock, Jonathan and Hamilton, Kathleen E.},
abstractNote = {A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.},
doi = {10.3390/e19090500},
journal = {Entropy},
issn = {1099-4300},
number = 9,
volume = 19,
place = {United States},
year = {2017},
month = {9}
}

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