Quantum Long Short-Term Memory for Drug Discovery

Quantum computing combined with machine learning (ML) is an extremely promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we successfully apply QML to drug discover...

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Bibliographic Details
Published inarXiv.org
Main Authors Zhang, Liang, Xu, Yin, Wu, Mohan, Wang, Liang, Xu, Hua
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.07.2024
Subjects
Machine learning
Quantum computing
Qubits (quantum computing)
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Summary:Quantum computing combined with machine learning (ML) is an extremely promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we successfully apply QML to drug discovery, showing that QML can significantly improve model performance and achieve faster convergence compared to classical ML. Moreover, we demonstrate that the model accuracy of the QML improves as the number of qubits increases. We also introduce noise to the QML model and find that it has little effect on our experimental conclusions, illustrating the high robustness of the QML model. This work highlights the potential application of quantum computing to yield significant benefits for scientific advancement as the qubit quantity increase and quality improvement in the future.
ISSN:2331-8422