Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping

ORAL

Abstract

We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. Our algorithm bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian in a way that transforms the noise attractor into higher-quality solutions. We present an improved Quantum Approximate Optimization Algorithm (QAOA) runs in experiments on Rigetti's quantum device. We report approximation ratios 0.9-0.96 for random, fully connected graphs on n=82 qubits, using only depth p=1 QAOA with NDAR. This compares to 0.34-0.51 for standard p=1 QAOA with the same number of function calls. The submission is based on preprint arXiv:2404.01412.

*This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00112090058 and IAA8839, Annex 114. Authors acknowledge support under NASA Academic Mission Services under contract No. NNA16BD14C.

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Publication: The preprint is available at https://arxiv.org/abs/2404.01412.

Presenters

  • Filip Bartosz Maciejewski

    • NASA; USRA

Authors

  • Filip Bartosz Maciejewski

    • NASA; USRA

  • Davide Venturelli

    • USRA Research Institute for Advanced Computer Science
    • NASA Ames Research Center

  • Stuart Hadfield

    • NASA; USRA

  • Jacob Biamonte

    • NASA; USRA