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The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model

Authors Joao Basso , Edward Farhi, Kunal Marwaha , Benjamin Villalonga , Leo Zhou

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Author Details

Joao Basso
  • Google Quantum AI, Venice, CA, USA
Edward Farhi
  • Google Quantum AI, Venice, CA, USA
  • Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
Kunal Marwaha
  • Department of Computer Science, University of Chicago, IL, USA
Benjamin Villalonga
  • Google Quantum AI, Venice, CA
Leo Zhou
  • Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA

Funding

  • Marwaha, Kunal: National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1746045. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Acknowledgements

The authors thank Sam Gutmann for being there and Matthew P. Harrigan for a careful read of the manuscript.

Cite As Get BibTex

Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou. The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model. In 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 232, pp. 7:1-7:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.TQC.2022.7

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to combinatorial optimization problems. Its performance monotonically improves with its depth p. We apply the QAOA to MaxCut on large-girth D-regular graphs. We give an iterative formula to evaluate performance for any D at any depth p. Looking at random D-regular graphs, at optimal parameters and as D goes to infinity, we find that the p = 11 QAOA beats all classical algorithms (known to the authors) that are free of unproven conjectures. While the iterative formula for these D-regular graphs is derived by looking at a single tree subgraph, we prove that it also gives the ensemble-averaged performance of the QAOA on the Sherrington-Kirkpatrick (SK) model defined on the complete graph. We also generalize our formula to Max-q-XORSAT on large-girth regular hypergraphs. Our iteration is a compact procedure, but its computational complexity grows as O(p² 4^p). This iteration is more efficient than the previous procedure for analyzing QAOA performance on the SK model, and we are able to numerically go to p = 20. Encouraged by our findings, we make the optimistic conjecture that the QAOA, as p goes to infinity, will achieve the Parisi value. We analyze the performance of the quantum algorithm, but one needs to run it on a quantum computer to produce a string with the guaranteed performance.

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum computation theory
  • Theory of computation → Approximation algorithms analysis
  • Mathematics of computing → Combinatorial optimization
Keywords
  • Quantum algorithm
  • Max-Cut
  • spin glass
  • approximation algorithm

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