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Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm
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DOI：10.1016/j.asoc.2023.110296.png)
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Optimization is one of the research areas where quantum computing could bring significant ben-efits. 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.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Genetic algorithms
Quantum Approximate Optimization
Algorithm
Quantum computing
Quantum optimization algorithms
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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