Shot Frugal Optimization for Variational Quantum-Classical Hybrid Algorithms

ORAL

Abstract

Variational hybrid quantum-classical algorithms (VHQCAs) seem likely to be the first useful algorithms in the era of near-term quantum computing. There is however a justified concern that the number of measurements needed for these algorithms to converge might become prohibitive when scaling up to non-trivial problem sizes. We address this issue by adapting results from classical optimization to the problem of shot-frugal optimization of VHQCAs. Specifically, we present new techniques and compare them with standard methods to demonstrate the potential for improvement both with noiseless and noisy quantum devices.

*This work was supported by the U.S. Department of Energy (DOE) through a quantum computing program sponsored by the Los Alamos National Laboratory (LANL) Information Science \& Technology Institute, the LDRD program at LANL, and the LANL ASC Beyond Moore's Law project. This work was also supported by the U.S. DOE, Office of Science, Office of Advanced Scientific Computing Research, under the Quantum Computing Application Teams program.

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Presenters

  • Andrew Arrasmith

    • Los Alamos National Laboratory

Authors

  • Andrew Arrasmith

    • Los Alamos National Laboratory

  • Jonas M Kubler

    • Max Planck Institute for Intelligent Systems

  • Lukasz Cincio

    • Los Alamos National Laboratory

  • Patrick J Coles

    • Los Alamos National Laboratory