Improve the Quantum Approximate Optimization Algorithm with Genetic Algorithm
Author:
Affiliation:
1. School of Applied Mathematics and Informatics, Ha Noi University of Science and Technology, Viet Nam
2. ISIMA, Université Clermont-Auvergne, CNRS, Mines de Saint-Etienne, Clermont Auvergne INP, LIMOS, France
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3628797.3628818
Reference23 articles.
1. Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm
2. Improving Variational Quantum Optimization using CVaR
3. Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models
4. Different Formulations for Solving the HeaviestK-Subgraph Problem
5. Lennart Bittel and Martin Kliesch . 2021. Training variational quantum algorithms is np-hard. Physical review letters 127, 12 ( 2021 ), 120502. Lennart Bittel and Martin Kliesch. 2021. Training variational quantum algorithms is np-hard. Physical review letters 127, 12 (2021), 120502.
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