Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms

Li, Tai-Yue; Mekala, Venugopala Reddy; Ng, Ka-Lok; Su, Cheng-Fang

doi:10.1109/bibe55377.2022.00078

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Presently, QML has demonstrated its significance in other areas of medicine, including precision medicine [8,9] and medical diagnostic systems [10][11][12][13][14][15][16]. In addition, several quantum algorithms, such as the variational quantum eigensolver (VQE) [17], have been employed in the drug discovery process.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

et al. 2023

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The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.

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Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

et al. 2023

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Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Avramouli¹,

Savvas²,

Vasilaki³

et al. 2023

Preprint

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The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of ML, leading to the emergence of Quantum Machine Learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals against the classical ones and a hybrid solution is the recommended approach at present.

show abstract

Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms

Cited by 2 publications

References 15 publications

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

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