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Benchmarking of Quantum SVM and Classical ML Algorithms for Prediction of Therapeutic Proteins

P. A. Tijare, Naman Kumar Mehta, Gajendra P. S. Raghava

Published May 7, 2025• 4 citations
Publisher

Abstract

Abstract Over the past decade, quantum machine learning, particularly quantum support vector machines (QSVMs), has emerged as an optimistic alternative to classical machine learning (CML) techniques. This study rigorously benchmarks the performance of QSVM and CML-based models across four diverse datasets relevant to therapeutic proteins and peptides. Specifically, we evaluated these approaches for the prediction of B-cell epitopes (CLBtope), exosomal proteins (ExoPropred), hemolytic peptides (HemoPI), and toxic peptides (Toxinpred3). The maximum area under the receiver operating characteristic curve (AUC) for the CLBtope dataset achieved was 0.68 for QSVM and 0.82 for CML models. For the ExoPropred dataset, the maximum AUCs were 0.66 (QSVM) and 0.72 (CML). In contrast, both QSVM and CML models demonstrated high performance on the HemoPI dataset, yielding maximum AUCs of 0.95 and 0.98, respectively. Similarly, for the Toxinpred3 dataset, the maximum AUCs were 0.84 (QSVM) and 0.94 (CML). All models were evaluated using independent validation datasets not used during training. These results suggest that although CML currently demonstrates superior predictive capability for these tasks, the similar progression in performance indicates potential for future advancements in QSVM. Highlights Comparative study of QSVM and CML models on four bioinformatics datasets QSVM performance tries to approach CML in tasks involving hemolytic and toxic peptide prediction Independent validation confirms robustness of performance metrics Results highlight the potential of QSVMs as real-world quantum hardware continues to matures

DOI
10.1101/2025.04.30.651419

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Abstract Over the past decade, quantum machine learning, particularly quantum support vector machines (QSVMs), has emerged as an optimistic alternative to classical machine learning (CML) techniques. This study rigorously benchmarks the performance of QSVM and CML-based models across four diverse datasets relevant to therapeutic proteins and peptides. Specifically, we evaluated these approaches for the prediction of B-cell epitopes (CLBtope), exosomal proteins (ExoPropred), hemolytic peptides (HemoPI), and toxic peptides (Toxinpred3). The maximum area under the receiver operating characteristic curve (AUC) for the CLBtope dataset achieved was 0.68 for QSVM and 0.82 for CML models. For the ExoPropred dataset, the maximum AUCs were 0.66 (QSVM) and 0.72 (CML). In contrast, both QSVM and CML models demonstrated high performance on the HemoPI dataset, yielding maximum AUCs of 0.95 and 0.98, respectively. Similarly, for the Toxinpred3 dataset, the maximum AUCs were 0.84 (QSVM) and 0.94 (CML). All models were evaluated using independent validation datasets not used during training. These results suggest that although CML currently demonstrates superior predictive capability for these tasks, the similar progression in performance indicates potential for future advancements in QSVM. Highlights Comparative study of QSVM and CML models on four bioinformatics datasets QSVM performance tries to approach CML in tasks involving hemolytic and toxic peptide prediction Independent validation confirms robustness of performance metrics Results highlight the potential of QSVMs as real-world quantum hardware continues to matures
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.
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 offers new opportunities for addressing complex classification tasks in biomedical applications. This study investigates two quantum machine learning models-the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC)-in the context of B-cell epitope prediction, a key step in modern vaccine design. QSVM builds upon the classical SVM framework by using quantum circuits to encode nonlinear kernel computations, while VQC replaces the entire classification pipeline with trainable quantum circuits optimized variationally. A benchmark dataset from the Immune Epitope Database (IEDB) is used for model evaluation. Each epitope is represented by 10 physicochemical features, and dimensionality reduction via Principal Component Analysis (PCA) is applied to assess model performance across different feature spaces. We also examine the effect of sample size on prediction outcomes. Experimental results show that QSVM performs well under limited data conditions, while VQC achieves higher accuracy in larger datasets. These findings highlight the potential of quantum-enhanced models for bioinformatics tasks, particularly in supporting efficient and scalable epitope-based vaccine development.
Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.
Quantum-assisted machine learning approaches have become a significant area of research in the healthcare domain by offering alternative solutions to classical methods, particularly when dealing with high-dimensional and complex datasets. This study presents a comparative evaluation of the classification performance of classical Support Vector Machines (SVM) and quantum-based algorithms Quantum Support Vector Machine (QSVM) and Pegasos-QSVM on healthcare data. Experimental analyses were conducted using three distinct medical datasets related to liver disease, breast cancer, and heart failure. The results demonstrate that the QSVM model consistently achieved the highest and most stable classification accuracy. Although the Pegasos-QSVM model achieved comparable accuracy rates in certain configurations, its performance was generally more variable. Nevertheless, thanks to its lower computational cost and faster processing time, Pegasos-QSVM emerges as a promising alternative, particularly in resource-constrained environments. The findings suggest that quantum-assisted models can deliver performance levels competitive with classical approaches, particularly highlighting the effectiveness of QSVM on small- to medium-sized datasets.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.