Hybrid Multi-Objective Genetic Programming for Parameterized Quantum Operator Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{gemeinhardt:2023:GECCOcomp,

• author = "Felix Guenther Gemeinhardt and Stefan Klikovits and Manuel Wimmer",
• title = "Hybrid {Multi-Objective} Genetic Programming for Parameterized Quantum Operator Discovery",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "795--798",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, quantum circuit synthesis, hybrid search, search-based quantum software engineering: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590696",
• size = "4 pages",
• abstract = "The processing of quantum information is defined by quantum circuits. For applications on current quantum devices, these are usually parameterized, i.e., they contain operations with variable parameters. The design of such quantum circuits and aggregated higher-level quantum operators is a challenging task which requires significant knowledge in quantum information theory, provided a polynomial-sized solution can be found analytically at all. Moreover, finding an accurate solution with low computational cost represents a significant trade-off, particularly for the current generation of quantum computers. To tackle these challenges, we propose a multi-objective genetic programming approach---hybridized with a numerical parameter optimizer---to automate the synthesis of parameterized quantum operators. To demonstrate the benefits of the proposed approach, it is applied to a quantum circuit of a hybrid quantum-classical algorithm, and then compared to an analytical solution as well as a non-hybrid version. The results show that, compared to the non-hybrid version, our method produces more diverse solutions and more accurate quantum operators which even reach the quality of the analytical baseline.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Felix Gemeinhardt Stefan Klikovits Manuel Wimmer

Citations