Abstract Well-known quantum machine learning techniques, specifically quantum kernel-assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparative study, it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. While QCNNs are superior in terms of training and prediction speed compared to QSVMs, the preference shifts toward QSVMs when the present noisy NISQ-era devices are incorporated into the comparison. QSVMs demonstrate superior overall performance compared to QCNNs when assessed using binary classification performance metrics. Classical methods are implemented to serve as a benchmark for comparison with the quantum approaches.
Abstract Well-known quantum machine learning techniques, specifically quantum kernel-assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparative study, it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. While QCNNs are superior in terms of training and prediction speed compared to QSVMs, the preference shifts toward QSVMs when the present noisy NISQ-era devices are incorporated into the comparison. QSVMs demonstrate superior overall performance compared to QCNNs when assessed using binary classification performance metrics. Classical methods are implemented to serve as a benchmark for comparison with the quantum approaches.
Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.
Abstract - We present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts. Specifically, we implement and analyze Quantum Support Vector Machines (QSVMs) vs classical SVMs, and Quantum Convolutional Neural Networks (QCNNs) vs classical CNNs. Using the widely used PlantVillage and complementary field datasets, we describe image preprocessing, classical baseline architectures, quantum data-encoding strategies, circuit-level QSVM and QCNN designs for near-term quantum devices, and hybrid training procedures. Where possible, we review literature-reported performance and propose a reproducible experimental pipeline for empirical evaluation on simulated/noisy quantum backends. We discuss expected strengths and limitations of quantum approaches (expressivity, kernel advantages, resource constraints), provide detailed evaluation metrics and ablations, and propose directions for real-device experiments and field deployment. Key takeaways: QSVM/quantum-kernel methods can provide superior separability on certain feature maps and small-to-medium-sized datasets, while QCNNs show promise as compact feature extractors for hybrid pipelines — but both approaches currently require careful circuit design and error-mitigation to outperform well-tuned classical models in realistic field settings. Key Words: QCNN, Plant Disease, SVM, CNN, QSVM
Abstract Pulsar stars, characterized by their highly magnetized and rapidly rotating compact nature, as well as their emission of radiation beams from magnetic poles, play a crucial role in astrophysics, particularly in the study of gravitational waves and general relativity. This paper investigates the application of machine learning techniques for the classification of pulsars using radio wave emission data from the HTRU-2 dataset, which contains an extensive collection of candidate samples. The study explores two quantum-assisted methodologies: a multi-qubit Quantum Approximate Optimization Algorithm (QAOA) inspired encoding and an innovative single-qubit Quantum Asymptotically Universal Multi-feature (QAUM) encoding within a quantum neural network framework. The performance evaluation encompasses a range of metrics, including accuracy, precision, recall, and specificity, which are analyzed across various optimizer configurations and learning rates. Notably, the QAUM approach consistently demonstrates superior sensitivity and reduced error rates compared to QAOA, particularly evident at specific learning rate settings. These findings underscore the transformative potential of quantum computing in advancing pulsar detection methodologies, highlighting its efficacy and implications for astrophysical data analysis and the broader field of quantum-enhanced scientific exploration.
The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm. This paper presents a comprehensive benchmarking study of classical and quantum machine learning models for image recognition on the MNIST handwritten digit dataset, evaluating both traditional models, a Classical Support Vector Machine (CSVM) and a Quantum Support Vector Machine (QSVM), and deep neural network models, a Classical Convolutional Neural Network (CCNN) and a Quantum Convolutional Neural Network (QCNN), across four performance dimensions: classification accuracy, computational runtime, parameter count, and memory requirements. Experiments are conducted as functions of both feature dimensionality and sample size, and across CPU and GPU execution environments, providing a controlled, multidimensional comparison to address gaps in prior work. For the SVM-based models, QSVM consistently outperforms CSVM in accuracy, reaching ∼ 0.90 versus ∼ 0.85 at 1,000 samples, with a higher computational cost. A feature count of 10 qubits and a sample size in the range of 200 -- 500 emerge as practical operating points that balance accuracy and runtime. For the neural network models, CCNN and QCNN achieve comparable classification accuracy, both exceeding 0.96 at 64 features and 60,000 samples, yet QCNN offers superior parameter and memory efficiency at higher feature counts, while incurring higher runtime. Across both model families, quantum models consistently outperform classical models by greater margins in accuracy as feature dimensionality or sample size increases.
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs’ application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs’ utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs’ robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques.
The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance.