• Corpus ID: 220845614

# Machine Learning Optimization of Quantum Circuit Layouts

@article{Paler2020MachineLO,
title={Machine Learning Optimization of Quantum Circuit Layouts},
author={Alexandru Paler and Lucian M. Sasu and Adrian Florea and Razvan Andonie},
journal={arXiv: Quantum Physics},
year={2020}
}
• Published 29 July 2020
• Computer Science
• arXiv: Quantum Physics
The quantum circuit layout problem is to map a quantum circuit to a quantum computing device, such that the constraints of the device are satisfied. The optimality of a layout method is expressed, in our case, by the depth of the resulting circuits. We introduce QXX, a novel search-based layout method, which includes a configurable Gaussian function used to: \emph{i)} estimate the depth of the generated circuits; \emph{ii)} determine the circuit region that influences most the depth. We…
6 Citations
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