A hybrid quantum-classical approach to warm-starting optimization

• URL: http://arxiv.org/abs/2309.13961v1
• Date: Mon, 25 Sep 2023 08:53:54 GMT
• Title: A hybrid quantum-classical approach to warm-starting optimization
• Authors: Vanessa Dehn and Thomas Wellens
• Abstract summary: We compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization. We show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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