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Cited by 564Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum many-body systems

Quantum approximate optimization of non-planar graph problems on a planar superconducting processor

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Harrigan, Matthew P., Sung, Kevin J., Neeley, Matthew, Satzinger, Kevin J., Arute, Frank, Arya, Kunal, Atalaya, Juan, Bardin, Joseph C. +78 more·2021·Nature Physics
DOI 10.1038/s41567-020-01105-y
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Crossref · 10.1038/s41567-020-01105-y

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  • Guerreschi and Matsuura raised scaling issues for QAOA.

    “With such peculiarities in mind, one should also view the results of Guerreschi and Matsuura, who found the point of quantum supremacy for QAOA to be in the order of several hundred to thousand of qubits [56] and further raised scaling issues [60].”

    From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [60]

    The Introduction (p. 2) explicitly highlights challenges in scaling QAOA, and Hardware Performance (p. 4) reports that performance on some problem types worsens as problem size increases. But the cited paper presents these as its own findings; the passages do not establish that Guerreschi and Matsuura raised those issues. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • By re-analyzing experimental data from Google's Sycamore quantum processor on QAOA for graph optimization, reachability deficits were discovered, with graph density replacing clause density as the order parameter.

    “Moreover, by re-analyzing the experimental data from Google's Sycamore quantum processor on the application of QAOA to various graph optimization problems (Harrigan et al. , 2021), authors from (Akshay et al. , 2020b) also discovered reachability deficits in this case, where the graph density (defined as the ratio between the number of graph edges to the number of graph nodes) replaces the clause”

    From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Harrigan et al., 2021

  • The optimal depth of QAOA as a tradeoff between ansatz accuracy and error rates has been found to be p=1 and p=3, respectively.

    “Thus, as an example, the optimal depth as a tradeoff between ansatz accuracy and error rates has been found to be p=1 and p=3, respectively [5, 60].”

    From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [60]

  • The effects of noise constitute an optimal depth for QAOA.

    “The effects of noise constitute an optimal depth for QAOA [5, 60].”

    From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [60]

  • Noisy computation leads to scaling issues for QAOA.

    “Noisy computation leads to scaling issues for QAOA [56, 60].”

    From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [60]

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Authors
Harrigan, M. P., Sung, K. J., Neeley, M., Satzinger, K. J., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Barends, R., Boixo, S., Broughton, M., Buckley, B. B., Buell, D. A., Burkett, B., Bushnell, N., Chen, Y., Chen, Z., Chiaro, B., Collins, R., Courtney, W., Demura, S., Dunsworth, A., Eppens, D., Fowler, A., Foxen, B., Gidney, C., Giustina, M., Graff, R., Habegger, S., Ho, A., Hong, S., Huang, T., Ioffe, L. B., Isakov, S. V., Jeffrey, E., Jiang, Z., Jones, C., Kafri, D., Kechedzhi, K., Kelly, J., Kim, S., Klimov, P. V., Korotkov, A. N., Kostritsa, F., Landhuis, D., Laptev, P., Lindmark, M., Leib, M., Martin, O., Martinis, J. M., McClean, J. R., McEwen, M., Megrant, A., Mi, X., Mohseni, M., Mruczkiewicz, W., Mutus, J., Naaman, O., Neill, C., Neukart, F., Niu, M. Y., O’Brien, T. E., O’Gorman, B., Ostby, E., Petukhov, A., Putterman, H., Quintana, C., Roushan, P., Rubin, N. C., Sank, D., Skolik, A., Smelyanskiy, V., Strain, D., Streif, M., Szalay, M., Vainsencher, A., White, T., Yao, Z. J., Yeh, P., Zalcman, A., Zhou, L., Neven, H., Bacon, D., Lucero, E., Farhi, E., & Babbush, R.
Affiliations
  • Google (United States) (US)
  • University of California, Riverside (US)
  • Data:Lab Munich (Germany) (DE)
  • Ames Research Center (US)
  • Leiden University (NL)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
Corresponding author
  • Harrigan, Matthew P.
  • Babbush, Ryan
ORCID
Venue
Nature Physics
Publisher
Springer Science and Business Media LLC
Published
2021-02-04
Volume
17
Issue
3
Pages
332-336
ISSN
  • 1745-2473 (ISSN-L)
  • 1745-2481
DOI
10.1038/s41567-020-01105-y
OpenAlex
W3015667706
Cited by
564
References
25
Citations by year
2026: 56 · 2025: 83 · 2024: 95 · 2023: 100 · 2022: 106 · 2021: 97 · 2020: 26
FWCI
33.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Physics (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Dave Bacon is a CIFAR Associate Fellow in the Quantum Information Science Program
  • EC | Horizon 2020 Framework Programme (828826)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
Keywords
quantum approximate optimization algorithm, combinatorial optimization, superconducting qubits, Sherrington-Kirkpatrick model, MaxCut, hardware compilation, approximation ratio, circuit depth

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