Local classical MAX-CUT algorithm outperforms $p=2$ QAOA on high-girth regular graphs

Kunal Marwaha

doi:10.22331/q-2021-04-20-437

Local classical MAX-CUT algorithm outperforms $p=2$ QAOA on high-girth regular graphs

Berkeley Center for Quantum Information and Computation, University of California, Berkeley, California 94720, USA

Published:	2021-04-20, volume 5, page 437
Eprint:	arXiv:2101.05513v2
Doi:	https://doi.org/10.22331/q-2021-04-20-437
Citation:	Quantum 5, 437 (2021).

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Abstract

The $p$-stage Quantum Approximate Optimization Algorithm (QAOA$_p$) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum (NISQ) devices, but its theoretical behavior is not well understood beyond $p=1$. We analyze QAOA$_2$ for the $\textit{maximum cut problem}$ (MAX-CUT), deriving a graph-size-independent expression for the expected cut fraction on any $D$-regular graph of girth $> 5$ (i.e. without triangles, squares, or pentagons).

We show that for all degrees $D \ge 2$ and every $D$-regular graph $G$ of girth $> 5$, QAOA$_2$ has a larger expected cut fraction than QAOA$_1$ on $G$. However, we also show that there exists a $2$-local randomized $\textit{classical}$ algorithm $A$ such that $A$ has a larger expected cut fraction than QAOA$_2$ on all $G$. This supports our conjecture that for every constant $p$, there exists a local classical MAX-CUT algorithm that performs as well as QAOA$_p$ on all graphs.

Featured image: A plot of the expected performance of QAOA and local classical algorithms on MAX-CUT for regular graphs with girth above 5. Just as a 1-local classical algorithm outperforms $p=1$ QAOA, a related 2-local classical algorithm outperforms $p=2$ QAOA.

For further reading go to our presentation Local Competitors to QAOA

► BibTeX data

@article{Marwaha2021localclassicalmax,
  doi = {10.22331/q-2021-04-20-437},
  url = {https://doi.org/10.22331/q-2021-04-20-437},
  title = {Local classical {MAX}-{CUT} algorithm outperforms {$p=2$} {QAOA} on high-girth regular graphs},
  author = {Marwaha, Kunal},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {5},
  pages = {437},
  month = apr,
  year = {2021}
}

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[1] Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko, "Equivariant quantum circuits for learning on weighted graphs", npj Quantum Information 9 1, 47 (2023).

[2] Jordi R. Weggemans, Alexander Urech, Alexander Rausch, Robert Spreeuw, Richard Boucherie, Florian Schreck, Kareljan Schoutens, Jiří Minář, and Florian Speelman, "Solving correlation clustering with QAOA and a Rydberg qudit system: a full-stack approach", Quantum 6, 687 (2022).

[3] Kunal Marwaha and Stuart Hadfield, "Bounds on approximating Max kXOR with quantum and classical local algorithms", Quantum 6, 757 (2022).

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[5] Zeqiao Zhou, Yuxuan Du, Xinmei Tian, and Dacheng Tao, "QAOA-in-QAOA: Solving Large-Scale MaxCut Problems on Small Quantum Machines", Physical Review Applied 19 2, 024027 (2023).

[6] Daniel J. Egger, Jakub Mareček, and Stefan Woerner, "Warm-starting quantum optimization", Quantum 5, 479 (2021).

[7] Phillip C. Lotshaw, Thien Nguyen, Anthony Santana, Alexander McCaskey, Rebekah Herrman, James Ostrowski, George Siopsis, and Travis S. Humble, "Scaling quantum approximate optimization on near-term hardware", Scientific Reports 12 1, 12388 (2022).

[8] Taylor L. Patti, Jean Kossaifi, Anima Anandkumar, and Susanne F. Yelin, "Variational quantum optimization with multibasis encodings", Physical Review Research 4 3, 033142 (2022).

[9] Xinwei Lee, Ningyi Xie, Dongsheng Cai, Yoshiyuki Saito, and Nobuyoshi Asai, "A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm", Mathematics 11 9, 2176 (2023).

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[12] Stuart Hadfield, Tad Hogg, and Eleanor G Rieffel, "Analytical framework for quantum alternating operator ansätze", Quantum Science and Technology 8 1, 015017 (2023).

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The above citations are from Crossref's cited-by service (last updated successfully 2023-05-29 20:37:24) and SAO/NASA ADS (last updated successfully 2023-05-29 20:37:25). The list may be incomplete as not all publishers provide suitable and complete citation data.

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