Quantum Machine Learning: Current State and Challenges

Avramouli, Maria; Savvas, Ilias Κ.; Garani, Georgia; Vasilaki, Anna

doi:10.1145/3503823.3503896

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…Research in the field of QML has grown over the past decade with technological advancement. Avramouli et al 9 presented an in‐depth review of the current research in the domain of QML and the challenges encountered in the field. The study explains the concept of QML and quantum embedding, representing classical data into quantum data.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional ML techniques have several limitations, such as inefficiency in handling complex problems and increased computation costs, and their performance is limited by the number of parameters they can handle. In recent years, the intersection of quantum computing and ML has led to the emergence of a new research area called quantum machine learning (QML) 9 . QML uses quantum computers, which are based on the principles of quantum mechanics, to perform various computations and predictions 10 .…”

Section: Introductionmentioning

confidence: 99%

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Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

Munshi,

Gupta,

Jadav

et al. 2023

Softw Pract Exp

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Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

Munshi,

Gupta,

Jadav

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…Electronics 2023, 12, x FOR PEER REVIEW 3 of 28 in [23,24] in chemistry. Finally, [25] was the first review that explored QML models applied to the drug discovery pipeline. In this review, the evolution of the use of QML algorithms in drug discovery is examined over the past five years.…”

Section: Drug Discoverymentioning

confidence: 99%

“…The review in [22] presented QC algorithms utilized in computational biology and in [23,24] in chemistry. Finally, [25] was the first review that explored QML models applied to the drug discovery pipeline.…”

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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“…Given the significance of these outcomes, quantum machine learning (QML) algorithms are expected to be capable of both recognising more complex patterns than classical ML algorithms [16,17], and training ML models more efficiently [18,19]. As a result, QML algorithms may provide the required alternative to classical ML by exploiting the principles and unique aspects of quantum mechanics to facilitate more accurate and efficient peptide classification and screening [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine

Zhuang,

Tanner,

et al. 2024

Preprint

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Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets,we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperform-ing the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification 1 task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.

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Quantum Machine Learning: Current State and Challenges

Cited by 5 publications

References 28 publications

Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine

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