Model-Driven Optimization for Quantum Program Synthesis with MOMoT

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

@InProceedings{Gemeinhardt:2023:MODELS-C,

• author = "Felix Gemeinhardt and Martin Eisenberg and Stefan Klikovits and Manuel Wimmer",
• booktitle = "2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)",
• title = "Model-Driven Optimization for Quantum Program Synthesis with {MOMoT}",
• year = "2023",
• pages = "614--621",
• abstract = "In the realm of classical software engineering, model-driven optimisation has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimisation can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist-e.g., based on reinforcement learning and genetic programming-these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimisation approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known Quantum algorithm.",
• keywords = "genetic algorithms, genetic programming, Computational modelling, Software algorithms, Quantum mechanics, Software systems, Space exploration, Quantum circuit, Integrated circuit modelling, Quantum Circuit Synthesis, Model-Driven Optimisation, Quantum Software Engineering",
• DOI = "doi:10.1109/MODELS-C59198.2023.00100",
• month = oct,
• notes = "Also known as \cite{10350515}",

}

Genetic Programming entries for Felix Gemeinhardt Martin Eisenberg Stefan Klikovits Manuel Wimmer

Citations