Quantum computer introduces a novel approach to process information. In quantum information processing, the law of quantum mechanics is applied to solve many practical computational problems. Classification is one such problem that can be resolved efficiently with the gate model quantum computer. There are several types of classifiers available in quantum domain, such as- variational quantum classifier (VQC), Quantum Support Vector Machine (QSVM) with Kernel Approximation, Hybrid Quantum Neural Network (QNN) etc. However, in this study, the mathematical similarities between VQC and classical support vector machine (SVM) and the components of the VQC are analyzed to optimize the performance of the classifier. For the convenience of the study, publicly available datasets, such as- IRIS dataset and Breast cancer dataset, are used in the experiments. IRIS dataset is brought into play for the testing and breast cancer dataset dimension reduced by Principle component analysis (PCA) is for validity test of the optimized VQC. After studying the VQC components in detail, it is found that the optimized VQC outperforms some of the classical machine learning algorithms or sometimes works as similar as classical SVM. The optimized VQC algorithm classifies IRIS dataset with 100% of accuracy and PCA dimension reduced Breast cancer dataset with 90% of accuracy. All of these studies are conducted with the help of Qiskit- an open-source software development kit (SKD) which is developed by IBM. So, the quantum device is considered to be ideal in every experiment.
Quantum computer introduces a novel approach to process information. In quantum information processing, the law of quantum mechanics is applied to solve many practical computational problems. Classification is one such problem that can be resolved efficiently with the gate model quantum computer. There are several types of classifiers available in quantum domain, such as- variational quantum classifier (VQC), Quantum Support Vector Machine (QSVM) with Kernel Approximation, Hybrid Quantum Neural Network (QNN) etc. However, in this study, the mathematical similarities between VQC and classical support vector machine (SVM) and the components of the VQC are analyzed to optimize the performance of the classifier. For the convenience of the study, publicly available datasets, such as- IRIS dataset and Breast cancer dataset, are used in the experiments. IRIS dataset is brought into play for the testing and breast cancer dataset dimension reduced by Principle component analysis (PCA) is for validity test of the optimized VQC. After studying the VQC components in detail, it is found that the optimized VQC outperforms some of the classical machine learning algorithms or sometimes works as similar as classical SVM. The optimized VQC algorithm classifies IRIS dataset with 100% of accuracy and PCA dimension reduced Breast cancer dataset with 90% of accuracy. All of these studies are conducted with the help of Qiskit- an open-source software development kit (SKD) which is developed by IBM. So, the quantum device is considered to be ideal in every experiment.
Early and accurate diagnosis of breast cancer plays a critical role in improving patient survival rates and reducing treatment costs. Classical machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM) have demonstrated strong performance in medical diagnosis tasks; however, their scalability and representational limits motivate the exploration of emerging paradigms. Quantum Machine Learning (QML), particularly Variational Quantum Classifiers (VQC), has recently gained attention due to its potential to model complex decision boundaries using quantum circuits. This paper presents a comparative analysis between a Variational Quantum Classifier and classical machine learning models for binary breast cancer classification. The study employs dimensionality-reduced clinical data and evaluates model performance using accuracy, confusion matrices, decision boundary visualization, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curves. Experimental results indicate that the VQC achieves competitive performance with classical approaches, demonstrating its feasibility for small-scale medical datasets and highlighting the promise of quantum-enhanced learning in healthcare applications.
Abstract This study presents a comparison between classical machine learning (ML) algorithms and their quantum-enhanced counterparts in classifying scikit’s breast cancer dataset. Specifically, the research focused on evaluating the accuracy and time computational efficiency of quantum-enhanced machine learning (QEML) algorithms against traditional or classical machine learning algorithms. The quantum algorithms employed include the quantum support vector machine (qSVM), variational quantum classifier (VQC) and quantum k-nearest neighbor (qkNN). The qSVM and qkNN algorithms discussed here are really kernel-based hybrid classical-quantum algorithms as opposed to fully quantum algorithms and the VQC is a hybrid classical-quantum algorithm. These algorithms were tested against their classical equivalents: support vector machine (SVM), k-nearest neighbor (kNN), and artificial neural network (ANN). The qSVM algorithm demonstrated an 90% accuracy in distinguishing malignant from benign cancer cases (the classical support vector machine algorithm had a 99% accuracy), while qkNN achieved 90% accuracy (the classical kNN algorithm had an accuracy of 97%), and VQC reported 88.77± 0.26% (averaged over 5 runs) (this was compared with an ANN which had an accuracy of 96%). The accuracies of the classical SVM, kNN, and ANN algorithms were recorded, to provide a basis for comparison. The results indicated that while QEML algorithms perform reasonably well in classification tasks, however they do not consistently outperform classical algorithms. Additionally, the study measured the time efficiency of each algorithm. The ANN required 33.8 seconds, kNN required 0.21 seconds, and SVM required 1.98 seconds to perform the classification tasks. In contrast, the quantum algorithms were significantly slower, with the VQC taking 632.53 seconds, qkNN requiring 495.34 seconds, and qSVM requiring 538.05 seconds (This is not fixed per run but give an impression of the time scales to run the algorithms). We emphasize that this simulation runtime does not reflect actual quantum hardware performance, where gate execution and circuit depth constraints differ significantly. To mitigate this ambiguity, we distinguish between simulation runtime and estimated quantum runtime where relevant. This work potentially highlights the cases where it may not offer any advantage to use quantum machine learning algorithms. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) analysis are also considered in this work where the values measured for the SVM, kNN and ANN were 99%, 99% and 99% respectively and for the qSVM, qkNN and VQC were 87%, 86% and 86± 0.2% (averaged over 5 runs) respectively. Overall, this research contributes valuable insights into the comparative performance of classical and quantum ML algorithms in a specific application, underscoring the potential and current limitations of quantum approaches in machine learning tasks. Hyperparameter optimization was done for all the algorithms, both quantum and classical, with Optuna. This was primarily because the QEML algorithms had more parameters and required considerable computational resources to optimize. This could have influenced the performance outcomes and is the basis for future work. The development of key comparison criteria is critical in the development of quantum machine learning, and this work offers some building blocks for this.
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.
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.
While quantum machine learning (QML) is a burgeoning field for solving intricate computational problems, its utility for actual medical diagnosis—where hardware is currently limited—is still being debated. This study explores the viability of a hybrid classical–quantum system for identifying breast masses via a Variational Quantum Classifier (VQC). Using a real-world breast cancer dataset, we established a binary task to separate benign from malignant cases. We handled clinical variables through standard encoding and normalization, then applied Principal Component Analysis (PCA) to compress the data into a format manageable for a low-qubit system. The quantum model, built on a parameterized variational circuit, was executed via a quantum simulator and benchmarked against a standard Support Vector Machine (SVM). Our findings show that the classical SVM outperformed the quantum approach (82.08% vs. 52.50%). However, the VQC still surpassed the threshold of random chance, even when restricted to only four qubits and minimal circuit depth.
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist's perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.