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Analytical Expressions for the Quantum Approximate Optimization Algorithm and its Variants

arXiv
Authors: Truman Yu Ng, Jin Ming Koh, Dax Enshan Koh

Year

2024

Paper ID

36739

Status

Preprint

Abstract Read

~2 min

Abstract Words

183

Citations

N/A

Abstract

The quantum approximate optimization algorithm (QAOA) is a near-term quantum algorithm aimed at solving combinatorial optimization problems. Since its introduction, various generalizations have emerged, spanning modifications to the initial state, phase unitaries, and mixer unitaries. In this work, we present an analytical study of broad families of QAOA variants. We begin by examining a family of QAOA with product mixers, which includes single-body mixers parametrized by multiple variational angles, and derive exact analytical expressions for the cost expectation on weighted problem graphs in the single-layer ansatz setting. We then analyze a family of QAOA that employs many-body Grover-type mixers, deriving analogous analytical expressions for weighted problem hypergraphs in the setting of arbitrarily many circuit ansatz layers. For both families, we allow individual phase angles for each node and edge (hyperedge) in the problem graph (hypergraph). Our results reveal that, in contrast to product mixers, the Grover mixer is sensitive to contributions from cycles of all lengths in the problem graph, exhibiting a form of non-locality. Our study advances the understanding of QAOA's behavior in general scenarios, providing a foundation for further theoretical exploration.

Why This Paper Matters

  • This paper contributes to the Quantum Foundations research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • The quantum approximate optimization algorithm (QAOA) is a near-term quantum algorithm aimed at solving combinatorial optimization problems.

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