Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem

Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem. Shaydulin, R., Li, C., Chakrabarti, S., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., Alexeev, Y., DeCross, M., Dreiling, J. M., Gaebler, J. P., Gatterman, T. M., Gerber, J. A., Gilmore, K., Gresh, D., Hewitt, N., Horst, C. V., Hu, S., Johansen, J., Matheny, M., Mengle, T., Mills, M., Moses, S. A., Neyenhuis, B., Siegfried, P., Yalovetzky, R., & Pistoia, M. Under review, 2023.

Paper doi bibtex

@article{Shaydulin2023,
  doi = {10.48550/arXiv.2308.02342},
  url = {https://arxiv.org/abs/2307.XXXXX},
  author = {Ruslan Shaydulin and Changhao Li and Shouvanik Chakrabarti and
    Dylan Herman and Niraj Kumar and Jeffrey Larson and Danylo Lykov and Pierre
      Minssen and Yue Sun and Yuri Alexeev and Matthew DeCross and Joan M.
      Dreiling and John P. Gaebler and Thomas M. Gatterman and Justin A. Gerber
      and Kevin Gilmore and Dan Gresh and Nathan Hewitt and Chandler V. Horst
      and Shaohan Hu and Jacob Johansen and Mitchell Matheny and Tanner Mengle
      and Michael Mills and Steven A. Moses and Brian Neyenhuis and Peter
      Siegfried and Romina Yalovetzky and Marco Pistoia},
  title = {Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem},
  year = {2023},
  journal = {Under review},
}

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