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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.07.2023
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 110296 |
| Author | Acampora, Giovanni Vitiello, Autilia Chiatto, Angela |
| Author_xml | – sequence: 1 givenname: Giovanni surname: Acampora fullname: Acampora, Giovanni email: giovanni.acampora@unina.it – sequence: 2 givenname: Angela surname: Chiatto fullname: Chiatto, Angela – sequence: 3 givenname: Autilia surname: Vitiello fullname: Vitiello, Autilia |
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| Keywords | Quantum computing Quantum Approximate Optimization Algorithm Quantum optimization algorithms Genetic algorithms |
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