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arXiv:2402.03847 · Quantum Information Processing

Non-hemolytic peptide classification using a quantum support vector machine

Shengxin Zhuang, John J. Tanner, Yusen Wu, Du Q. Huynh, Wei Liu, Xavier F. Cadet, Nicolas Fontaine, Philippe Charton, Cédric Damour, Frédéric Cadet, Jingbo B. Wang

Published Nov 20, 2024• 7 citations
arXivHTMLPDF

Abstract

Abstract 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.

DOI
10.1007/s11128-024-04540-5

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Abstract 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 is an active research field where the power of quantum mechanics is integrated into machine learning algorithms to solve problems that are intractable for classical computers. Quantum support vector machine (QSVM) was one of the first quantum algorithms that got implemented on real quantum hardware. It demonstrated promising outcomes as compared to classical Support vector machine (SVM) through many implementations. However, it encounters challenges in scaling with large datasets and in resource effectiveness when compare to the usage of innovative classical algorithms in the Noisy Intermediate Scale Quantum era. Here is a review of some latest developments of QSVM and discussion on prospects of QSVM application in the future.
Quantum machine learning has emerged as a promising paradigm that leverages quantum computational principles to potentially outperform classical approaches on specific learning tasks. This paper presents a comprehensive empirical investigation comparing Quantum Machine Learning (QML), specifically Quantum Support Vector Machines (QSVM) against Classical Machine Learning (CML) baselines including Logistic Regression, K Nearest Neighbors and classical SVMs. We implement quantum kernel methods using variational quantum circuits with angle embedding, IQP and ZZ feature maps on datasets exhibiting varying structural complexity. Our experiments on synthetic quantum generated datasets (Quantum Parity, Quantum Entanglement, Quantum Interference) demonstrate that QSVM achieves superior classification accuracy with improvements ranging from 2.9 % to 60 % over the best classical baselines achieving a 55.6 % overall win rate. Notably, on the Quantum Parity dataset, QSVM consistently outperformed all classical models across multiple configurations. On real-world benchmark datasets (Breast Cancer, Iris, Wine), QSVM Angle achieved 88% accuracy on Breast Cancer compared to 76% for classical SVM Linear a 15.8 % relative improvement while matching the best classical performance on Iris(100%)(100 \%)(100%)and Wine(92%)(92 \%)(92%). All experiments were conducted using quantum circuit simulation on classical hardware. These results provide empirical evidence that quantum kernel methods offer meaningful advantages over classical approaches on datasets with complex feature interactions. We discuss the theoretical foundations of these improvements including quantum feature space expressivity and kernel alignment.
Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.
Quantum computing has demonstrated remarkable prowess in addressing factorization issues and unordered search problems, showcasing its quantum parallelism capabilities that enable exponential speed-up for specific computational challenges. However, the seamless integration of classical and quantum computing to harness accelerated computation speed presents unique challenges. This paper delves into the intricacies of this integration, focusing on the current state of quantum machine learning (QML) and evaluating the performance of classical and quantum algorithms in terms of accuracy. To address these challenges, we conducted experiments utilizing three datasets for binary classification, implementing both classical Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our investigations reveal that the QSVM algorithm consistently outperforms its classical counterpart on complex datasets. Notably, the performance gap between quantum and classical models widens with increasing dataset complexity, shedding light on the susceptibility of simple classical models to overfitting when confronted with intricate datasets.Despite the ongoing development required for quantum hardware with ample resources, our findings underscore the significant potential of quantum machine learning, particularly in unsupervised learning and generative models. As we move forward, it is imperative to channel more efforts into exploring novel quantum learning models capable of harnessing the inherent power of quantum mechanics to overcome the constraints of classical machine learning. This research contributes to the ongoing discourse on the future of quantum computing, emphasizing the need for continued exploration and innovation in quantum machine learning methodologies.