A hybrid quantum-classical approach to warm-starting optimization

Vanessa Dehn; Thomas Wellens

doi:10.1117/12.3002220

13 March 2024 A hybrid quantum-classical approach to warm-starting optimization

Vanessa Dehn, Thomas Wellens

Proceedings Volume 12911, Quantum Computing, Communication, and Simulation IV; 129110N (2024) https://doi.org/10.1117/12.3002220
Event: SPIE Quantum West, 2024, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Vanessa Dehn and Thomas Wellens "A hybrid quantum-classical approach to warm-starting optimization", Proc. SPIE 12911, Quantum Computing, Communication, and Simulation IV, 129110N (13 March 2024); https://doi.org/10.1117/12.3002220

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