2024 | OriginalPaper | Buchkapitel
An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation
verfasst von : Zakaria Abdelmoiz Dahi, Francisco Chicano, Gabriel Luque
Erschienen in: Applications of Evolutionary Computation
Verlag: Springer Nature Switzerland
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Abstract
(I)
devising a complete Machine Learning pipeline including the ETL component and the evolutionary deep neural model using the linkage learning algorithm P3
, (II)
a modelling applicable to any quantum algorithm with a special interest to both optimisation and machine learning ones, (III)
diverse and fresh benchmarks using calibration data of four real IBM quantum computers collected over 10 months (Dec. 2022 and Oct. 2023) and training dataset built using four types of quantum optimisation and machine learning algorithms, as well as random ones. The proposal has been proven to be more efficient and simple than state-of-the-art deep neural models in the literature.