Recursive QAOA outperforms the original QAOA for the MAX-CUT problem on complete graphs

Quantum approximate optimization algorithms are hybrid quantum-classical variational algorithms designed to approximately solve combinatorial optimization problems such as the MAX-CUT problem. In spite of its potential for near-term quantum applications, it has been known that quantum approximate op...

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Bibliographic Details
Published in:Quantum information processing Vol. 23; no. 3
Main Authors: Bae, Eunok, Lee, Soojoon
Format: Journal Article
Language:English
Published: New York Springer US 26.02.2024
Subjects:
Data Structures and Information Theory
Mathematical Physics
Physics
Physics and Astronomy
Quantum Computing
Quantum Information Technology
Quantum Physics
Spintronics
Complete graphs
68Q12
81P68
Recursive quantum approximate optimization algorithms
MAX-CUT problem
ISSN:1573-1332, 1573-1332
Online Access:Get full text
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Summary:Quantum approximate optimization algorithms are hybrid quantum-classical variational algorithms designed to approximately solve combinatorial optimization problems such as the MAX-CUT problem. In spite of its potential for near-term quantum applications, it has been known that quantum approximate optimization algorithms have limitations for certain instances to solve the MAX-CUT problem, at any constant level p . Recently, the recursive quantum approximate optimization algorithm, which is a non-local version of quantum approximate optimization algorithm, has been proposed to overcome these limitations. However, it has been shown by mostly numerical evidences that the recursive quantum approximate optimization algorithm outperforms the original quantum approximate optimization algorithm for specific instances. In this paper, we analytically prove that the recursive quantum approximate optimization algorithm is more competitive than the original one to solve the MAX-CUT problem for complete graphs with respect to the approximation ratio.
ISSN:1573-1332
1573-1332
DOI:10.1007/s11128-024-04286-0