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Title: Optimizing adiabatic quantum program compilation using a graph-theoretic framework

Abstract

Adiabatic quantum computing has evolved in recent years from a theoretical field into an immensely practical area, a change partially sparked by D-Wave System’s quantum annealing hardware. These multimillion-dollar quantum annealers offer the potential to solve optimization problems millions of times faster than classical heuristics, prompting researchers at Google, NASA and Lockheed Martin to study how these computers can be applied to complex real-world problems such as NASA rover missions. Unfortunately, compiling (embedding) an optimization problem into the annealing hardware is itself a difficult optimization problem and a major bottleneck currently preventing widespread adoption. Additionally, while finding a single embedding is difficult, no generalized method is known for tuning embeddings to use minimal hardware resources. To address these barriers, we introduce a graph-theoretic framework for developing structured embedding algorithms. Using this framework, we introduce a biclique virtual hardware layer to provide a simplified interface to the physical hardware. Additionally, we exploit bipartite structure in quantum programs using odd cycle transversal (OCT) decompositions. By coupling an OCT-based embedding algorithm with new, generalized reduction methods, we develop a new baseline for embedding a wide range of optimization problems into fault-free D-Wave annealing hardware. Furthermore, to encourage the reuse and extension of thesemore » techniques, we provide an implementation of the framework and embedding algorithms.« less

Authors:
 [1];  [1]; ORCiD logo [2]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1460229
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Quantum Information Processing
Additional Journal Information:
Journal Volume: 17; Journal Issue: 5; Journal ID: ISSN 1570-0755
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Goodrich, Timothy D., Sullivan, Blair D., and Humble, Travis S. Optimizing adiabatic quantum program compilation using a graph-theoretic framework. United States: N. p., 2018. Web. doi:10.1007/s11128-018-1863-4.
Goodrich, Timothy D., Sullivan, Blair D., & Humble, Travis S. Optimizing adiabatic quantum program compilation using a graph-theoretic framework. United States. doi:10.1007/s11128-018-1863-4.
Goodrich, Timothy D., Sullivan, Blair D., and Humble, Travis S. Sat . "Optimizing adiabatic quantum program compilation using a graph-theoretic framework". United States. doi:10.1007/s11128-018-1863-4. https://www.osti.gov/servlets/purl/1460229.
@article{osti_1460229,
title = {Optimizing adiabatic quantum program compilation using a graph-theoretic framework},
author = {Goodrich, Timothy D. and Sullivan, Blair D. and Humble, Travis S.},
abstractNote = {Adiabatic quantum computing has evolved in recent years from a theoretical field into an immensely practical area, a change partially sparked by D-Wave System’s quantum annealing hardware. These multimillion-dollar quantum annealers offer the potential to solve optimization problems millions of times faster than classical heuristics, prompting researchers at Google, NASA and Lockheed Martin to study how these computers can be applied to complex real-world problems such as NASA rover missions. Unfortunately, compiling (embedding) an optimization problem into the annealing hardware is itself a difficult optimization problem and a major bottleneck currently preventing widespread adoption. Additionally, while finding a single embedding is difficult, no generalized method is known for tuning embeddings to use minimal hardware resources. To address these barriers, we introduce a graph-theoretic framework for developing structured embedding algorithms. Using this framework, we introduce a biclique virtual hardware layer to provide a simplified interface to the physical hardware. Additionally, we exploit bipartite structure in quantum programs using odd cycle transversal (OCT) decompositions. By coupling an OCT-based embedding algorithm with new, generalized reduction methods, we develop a new baseline for embedding a wide range of optimization problems into fault-free D-Wave annealing hardware. Furthermore, to encourage the reuse and extension of these techniques, we provide an implementation of the framework and embedding algorithms.},
doi = {10.1007/s11128-018-1863-4},
journal = {Quantum Information Processing},
number = 5,
volume = 17,
place = {United States},
year = {2018},
month = {4}
}

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