Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm

Optimization is one of the research areas where quantum computing could bring significant benefits. In this scenario, a hybrid quantum–classical variational algorithm, the Quantum Approximate Optimization Algorithm (QAOA), is receiving much attention for its potential to efficiently solve combinator...

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Veröffentlicht in:Applied soft computing Jg. 142; S. 110296
Hauptverfasser: Acampora, Giovanni, Chiatto, Angela, Vitiello, Autilia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2023
Schlagworte:
Genetic algorithms
Quantum Approximate Optimization Algorithm
Quantum computing
Quantum optimization algorithms
Quantum computing
Quantum Approximate Optimization Algorithm
Quantum optimization algorithms
Genetic algorithms
ISSN:1568-4946, 1872-9681
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Zusammenfassung:Optimization is one of the research areas where quantum computing could bring significant benefits. In this scenario, a hybrid quantum–classical variational algorithm, the Quantum Approximate Optimization Algorithm (QAOA), is receiving much attention for its potential to efficiently solve combinatorial optimization problems. This approach works by using a classical optimizer to identify appropriate parameters of a problem-dependent quantum circuit, which ultimately performs the optimization process. Unfortunately, learning the most appropriate QAOA circuit parameters is a complex task that is affected by several issues, such as search landscapes characterized by many local optima. Moreover, gradient-based optimizers, which have been pioneered in this context, tend to waste quantum computing resources. Therefore, gradient-free approaches are emerging as promising methods to address this parameter-setting task. Following this trend, this paper proposes, for the first time, the use of genetic algorithms as gradient-free methods for optimizing the QAOA circuit. The proposed evolutionary approach has been evaluated in solving the MaxCut problem for graphs with 5 to 9 nodes on a noisy quantum device. As the results show, the proposed genetic algorithm statistically outperforms the state-of-the-art gradient-free optimizers by achieving solutions with a better approximation ratio. •A genetic algorithm is proposed to optimize the gate parameters of QAOA.•The QAOA optimized by a genetic algorithm is applied to solve the MaxCut problem.•The proposed genetic algorithm outperforms state-of-the-art gradientfree methods.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2023.110296