Investigations of quantum heuristics for optimization

ORAL

Abstract

We explore the design of quantum heuristics for optimization, focusing on the quantum approximate optimization algorithm, a metaheuristic developed by Farhi, Goldstone, and Gutmann. We develop specific instantiations of the of quantum approximate optimization algorithm for a variety of challenging combinatorial optimization problems. Through theoretical analyses and numeric investigations of select problems, we provide insight into parameter setting and Hamiltonian design for quantum approximate optimization algorithms and related quantum heuristics, and into their implementation on hardware realizable in the near term.

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Authors

  • Eleanor Rieffel

    • NASA Ames Research Center

  • Stuart Hadfield

    • Columbia University

  • Zhang Jiang

    • NASA Ames Research Center

  • Salvatore Mandr{\`a}

    • NASA Ames Research Center
    • NASA Ames Research Center Quantum Artificial Intelligence Laboratory (QuAIL) Stinger Ghaffarian Technologies Inc.

  • Davide Venturelli

    • NASA Ames Research Center

  • Zhihui Wang

    • NASA Ames Research Center