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Quantum Optimization

A hybrid quantum-classical approach to warm-starting optimization

arXiv

Authors: Vanessa Dehn, Thomas Wellens

Year

2023

Paper ID

54481

Status

Preprint

Abstract Read

~2 min

Abstract Words

105

Citations

N/A

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting the standard algorithm improves the performance. In this paper we compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization and investigate the warm-start approach for different problem instances. In particular, we analyze the extent to which the improved performance of warm-start QAOA is due to quantum effects, and show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.

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This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2309.13961 [2] arXiv https://arxiv.org/abs/2309.13961 [3] Publisher https://arxiv.org/abs/2309.13961

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