Adiabatic quantum programming: minor embedding with hard faults

Adiabatic quantum programming: minor embedding with hard faults Adiabatic quantum programming defines the time-dependent mapping of a quantum algorithm into an underlying hardware or logical fabric. An essential step is embedding problem-specific information into the quantum logical fabric. We present algorithms for embedding arbitrary instances of the adiabatic quantum optimization algorithm into a square lattice of specialized unit cells. These methods extend with fabric growth while scaling linearly in time and quadratically in footprint. We also provide methods for handling hard faults in the logical fabric without invoking approximations to the original problem and illustrate their versatility through numerical studies of embeddability versus fault rates in square lattices of complete bipartite unit cells. The studies show that these algorithms are more resilient to faulty fabrics than naive embedding approaches, a feature which should prove useful in benchmarking the adiabatic quantum optimization algorithm on existing faulty hardware. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quantum Information Processing Springer Journals

Adiabatic quantum programming: minor embedding with hard faults

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Publisher
Springer US
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Physics; Quantum Information Technology, Spintronics; Quantum Computing; Data Structures, Cryptology and Information Theory; Quantum Physics; Mathematical Physics
ISSN
1570-0755
eISSN
1573-1332
D.O.I.
10.1007/s11128-013-0683-9
Publisher site
See Article on Publisher Site

Abstract

Adiabatic quantum programming defines the time-dependent mapping of a quantum algorithm into an underlying hardware or logical fabric. An essential step is embedding problem-specific information into the quantum logical fabric. We present algorithms for embedding arbitrary instances of the adiabatic quantum optimization algorithm into a square lattice of specialized unit cells. These methods extend with fabric growth while scaling linearly in time and quadratically in footprint. We also provide methods for handling hard faults in the logical fabric without invoking approximations to the original problem and illustrate their versatility through numerical studies of embeddability versus fault rates in square lattices of complete bipartite unit cells. The studies show that these algorithms are more resilient to faulty fabrics than naive embedding approaches, a feature which should prove useful in benchmarking the adiabatic quantum optimization algorithm on existing faulty hardware.

Journal

Quantum Information ProcessingSpringer Journals

Published: Nov 20, 2013

References

  • A tourist guide through treewidth
    Bodlaender, HL
  • Pseudo-boolean optimization
    Boros, E; Hammer, PL
  • Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
    Choi, V

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