This study investigates quantum computing's role in B-cell epitope prediction using Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC). It highlights the potential of quantum machine learning in bioinformatics, addressing computational efficiency limitations of traditional methods as data complexity grows. QSVM uses quantum kernel functions for data mapping, while VQC employs parameterized quantum circuits for classification. Results show QSVM and VQC achieving 70% and 73% accuracy, respectively, with QSVM excelling in balancing classes. Despite challenges like computational demands and hardware limitations, quantum methods show promise, suggesting future improvements with ongoing advancements.
This study investigates quantum computing's role in B-cell epitope prediction using Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC). It highlights the potential of quantum machine learning in bioinformatics, addressing computational efficiency limitations of traditional methods as data complexity grows. QSVM uses quantum kernel functions for data mapping, while VQC employs parameterized quantum circuits for classification. Results show QSVM and VQC achieving 70% and 73% accuracy, respectively, with QSVM excelling in balancing classes. Despite challenges like computational demands and hardware limitations, quantum methods show promise, suggesting future improvements with ongoing advancements.
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.
A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning to enhance computing efficiency and the expressiveness of models. In this paper, two different quantum classifiers - Variational Quantum Classifier (VQC) and Quantum Kernel Support Vector Machine (QSVM) - are compared with three classical classifiers as baseline classifiers - Logistic Regression, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) - on the Breast Cancer Wisconsin dataset. The quantum circuits were created in the PennyLane framework and simulated on a classical backend. However, in terms of accuracy, classical Logistic Regression performed better with an accuracy of 97.8%, classical SVM and QSVM with an accuracy of 95.6% each, although the Quantum VQC achieved a lower accuracy of 88.9% and had a recall of 100% for the benign class, though it correctly identified only 12 of the 17 malignant cases (a malignant-class recall of approximately 70.6%). The drawback of quantum models is the higher training time; however, since the quantum circuit needs to be classically simulated, the quantum SVM took 23.29 seconds compared to less than 0.01 seconds for the classical linear models. These results indicate that for small structured datasets, classifiers based on quantum computing have not yet surpassed well-tuned classical counterparts. In some respects (e.g., benign-class recall), they perform competitively, though not on malignant-class recall, where the VQC in particular performed worse than the classical baselines, which is worth further investigation on real quantum computers.
Accurate disease diagnosis is becoming a critical challenge owing to the limited success of classical machine-learning approaches, especially in classification problems. Accurate and rapid brain tumor classification in medical diagnosis could aid in early detection and treatment planning. This paper presents a novel quantum-enhanced machine learning approach using a Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) models for improved tumor classification. ZFeatureMap is deployed for the conversion of classical data points to quantum space, as it is found to be most suitable for the characteristics of the brain tumor dataset and provides a balance between accuracy, efficiency, and computational feasibility. The QSVM and VQC models are implemented on a publicly available brain tumor dataset, demonstrating significant improvement over classical algorithms. Both models were implemented on a 32-qubit quantum simulator and a seven-qubit quantum computer. The simulation results indicate that the QSVM-based model is 3.62% more accurate and 60.4 times faster, while the VQC-based model is 2.49% more accurate and twice as fast as classical SVM. The findings suggest that quantum-enhanced machine learning has the scope to revolutionize medical diagnosis with more precise and faster results.
Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC.
Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks. Through a comparative analysis, we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality. As a case study, we explored the potential of quantum paradigms in the context of a real-world problem: classifying pancreatic cancer biomarker data. The Support Vector Classifier (SVC) algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework. The classical approach as well as the quantum-based techniques reported similar accuracy. This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset. Remarkably, quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency. This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.
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.