Optimizing the Prioritization of Compiled Quantum Circuits by Machine Learning Approaches-Reference-Cited by-同舟云学术

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Optimizing the Prioritization of Compiled Quantum Circuits by Machine Learning Approaches

Published:2022 Issue: Volume: Page:161-181
ISSN:1865-0929
Container-title:Service-Oriented Computing
language:
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Author:

Salm Marie^ORCID,Barzen Johanna^ORCID,Leymann Frank^ORCID,Wundrack Philipp^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-18304-1_9

Reference54 articles.

1. Dodge, Y.: Spearman rank correlation coefficient. In: Dodge, Y. (ed.) The Concise Encyclopedia of Statistics, pp. 502–505. Springer, New York (2008). https://doi.org/10.1007/978-0-387-32833-1_379

2. Aleksandrowicz, G., et al.: Qiskit: An Open-source Framework for Quantum Computing (2019). https://doi.org/10.5281/zenodo.2562111

3. Alpaydin, E.: Machine Learning: The New AI. MIT Press, Cambridge (2016)

4. Advances in Intelligent Systems and Computing;E Balugani,2020

5. Barzen, J.: From Digital Humanities to Quantum Humanities: Potentials and Applications (2022, to appear). https://doi.org/10.48550/ARXIV.2103.11825

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting Good Quantum Circuit Compilation Options;2023 IEEE International Conference on Quantum Software (QSW);2023-07

2. How to Select Quantum Compilers and Quantum Computers Before Compilation;Proceedings of the 13th International Conference on Cloud Computing and Services Science;2023

3. Distributing Quantum Computations, by Shots;Service-Oriented Computing;2023

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