Quantum machine learning models, like Quantum Neural Networks (QNN) and Quantum Support Vector Classifiers (QSVC), often struggle with overfitting, slow convergence, and suboptimal generalization across various datasets. This paper explores the advantages of integrating deep unfolding techniques into quantum models and develops a framework focusing on Deep Unfolded Variational Quantum Classifiers (DVQC), Deep Unfolded Quantum Neural Networks (DQNN), and Deep Unfolded QSVC (DQSVC). Our novel unfolding transforms quantum circuit training into a sequence of learnable layers, with each layer representing an optimization step that concurrently renews both circuit parameters and QNN hyperparameters. The proposed framework significantly improves training and test accuracy by dynamically adjusting learning rate, perturbations and other similar hyperparameters, particularly on complex datasets like Genomic and Breast Cancer. Our evaluation and experiment show that proposed DVQC and DQNN outperform baseline VQC and QNN, achieving 90% training accuracy and up to 20% higher test accuracy on Genomic and Adhoc datasets. DQSVC achieves 100% accuracy on Adhoc and 97% on Genomic datasets, surpassing the 90% test accuracy of traditional QSVC. Our implementation details will be publicly available.
Quantum machine learning models, like Quantum Neural Networks (QNN) and Quantum Support Vector Classifiers (QSVC), often struggle with overfitting, slow convergence, and suboptimal generalization across various datasets. This paper explores the advantages of integrating deep unfolding techniques into quantum models and develops a framework focusing on Deep Unfolded Variational Quantum Classifiers (DVQC), Deep Unfolded Quantum Neural Networks (DQNN), and Deep Unfolded QSVC (DQSVC). Our novel unfolding transforms quantum circuit training into a sequence of learnable layers, with each layer representing an optimization step that concurrently renews both circuit parameters and QNN hyperparameters. The proposed framework significantly improves training and test accuracy by dynamically adjusting learning rate, perturbations and other similar hyperparameters, particularly on complex datasets like Genomic and Breast Cancer. Our evaluation and experiment show that proposed DVQC and DQNN outperform baseline VQC and QNN, achieving 90% training accuracy and up to 20% higher test accuracy on Genomic and Adhoc datasets. DQSVC achieves 100% accuracy on Adhoc and 97% on Genomic datasets, surpassing the 90% test accuracy of traditional QSVC. Our implementation details will be publicly available.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
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.
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies
The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.
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.