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Template-Based Minor Embedding for Adiabatic Quantum Optimization

Author

Listed:
  • Thiago Serra

    (Bucknell University, Lewisburg, Pennsylvania 17837)

  • Teng Huang

    (Lingnan (University) College, Sun Yat-sen University, 510275 Guangzhou, People’s Republic of China)

  • Arvind U. Raghunathan

    (Mitsubishi Electric Research Laboratories, Cambridge, Massachusetts 02139)

  • David Bergman

    (University of Connecticut, Storrs, Connecticut 06269)

Abstract

Quantum annealing (QA) can be used to quickly obtain near-optimal solutions for quadratic unconstrained binary optimization (QUBO) problems. In QA hardware, each decision variable of a QUBO should be mapped to one or more adjacent qubits in such a way that pairs of variables defining a quadratic term in the objective function are mapped to some pair of adjacent qubits. However, qubits have limited connectivity in existing QA hardware. This has spurred work on preprocessing algorithms for embedding the graph representing problem variables with quadratic terms into the hardware graph representing qubits adjacencies, such as the Chimera graph in hardware produced by D-Wave Systems. In this paper, we use integer linear programming to search for an embedding of the problem graph into certain classes of minors of the Chimera graph, which we call template embeddings . One of these classes corresponds to complete bipartite graphs, for which we show the limitation of the existing approach based on minimum odd cycle transversals (OCTs). One of the formulations presented is exact and thus can be used to certify the absence of a minor embedding using that template. On an extensive test set consisting of random graphs from five different classes of varying size and sparsity, we can embed more graphs than a state-of-the-art OCT-based approach, our approach scales better with the hardware size, and the runtime is generally orders of magnitude smaller. Summary of Contribution: Our work combines classical and quantum computing for operations research by showing that integer linear programming can be successfully used as a preprocessing step for adiabatic quantum optimization. We use it to determine how a quadratic unconstrained binary optimization problem can be solved by a quantum annealer in which the qubits are coupled as in a Chimera graph, such as in the quantum annealers currently produced by D-Wave Systems. The paper also provides a timely introduction to adiabatic quantum computing and related work on minor embeddings.

Suggested Citation

  • Thiago Serra & Teng Huang & Arvind U. Raghunathan & David Bergman, 2022. "Template-Based Minor Embedding for Adiabatic Quantum Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 427-439, January.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:1:p:427-439
    DOI: 10.1287/ijoc.2021.1065
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    Cited by:

    1. Byron Tasseff & Tameem Albash & Zachary Morrell & Marc Vuffray & Andrey Y. Lokhov & Sidhant Misra & Carleton Coffrin, 2024. "On the emerging potential of quantum annealing hardware for combinatorial optimization," Journal of Heuristics, Springer, vol. 30(5), pages 325-358, December.

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