Compiled Quantum Optimization Algorithms in NISQ Processors

ORAL

Abstract

We discuss resource estimation and synthesis optimization results related to compilation of a variety of structured variational algorithms. Specifically, we look at software tools and methods for finding a swap network that allows the efficient execution of algorithms on different superconducting chips (Rigetti’s Aspen Chip, Google’s Sycamore, IBM’s Tokyo). Efficiency is measured in terms of the total temporal makespan of execution of the compiled quantum circuit. Examples include algorithms for scheduling and asset allocation with both soft and hard constraints. We address two different regimes: where near-optimal compilations can be found, and where only heuristics (e.g., temporal planning methods) are available.

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Presenters

  • Davide Venturelli

    QuAIL, USRA, NASA

Authors

  • Davide Venturelli

    QuAIL, USRA, NASA

  • Minh Do

    NASA Ames Research Center

  • Bryan O'Gorman

    University of California, Berkeley, Electrical Engineering and Computer Sciences, University of California, Berkeley, QuAIL, Berkeley University, NASA

  • Zhihui Wang

    NASA Ames Research Center, QuAIL, USRA, NASA

  • Eleanor Rieffel

    Quantum AI Lab, NASA Ames Research Center, QuAIL, NASA Ames Research Center, NASA Ames Research Center, Quantum AI Lab (QuAIL), NASA Ames Research Center, QuAIL, NASA

  • Jeremy Frank

    NASA Ames Research Center

  • Ryan M LaRose

    Michigan State University, QuAIL, UMich, USRA, NASA

  • Vanesa Gomez Gonzalez

    USRA