In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic sequence classification.
Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome sequence data by employing various feature mapping techniques. We present an open-source, independent Qiskit-based implementation to conduct experiments on a benchmark genomic dataset. Our simulations reveal that the interplay between feature mapping techniques and QML algorithms significantly influences performance. Notably, the Pegasos Quantum Support Vector Classifier (Pegasos-QSVC) exhibits high sensitivity, particularly excelling in recall metrics, while Quantum Neural Networks (QNN) achieve the highest training accuracy across all feature maps. However, the pronounced variability in classifier performance, dependent on feature mapping, highlights the risk of overfitting to localized output distributions in certain scenarios. This work underscores the transformative potential of QML for genomic data classification while emphasizing the need for continued advancements to enhance the robustness and accuracy of these methodologies.
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.
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
Genomic data analysis is essential for personalized medicine, disease diagnostics, and understanding evolutionary biology, yet remains challenging due to high dimensionality and sequence complexity. Quantum Machine Learning (QML) offers promising computational advantages for scalable genomic analysis. We explore QML modeling for genomic sequence classification using 100,000 sequences from two transcript classes. Our new framework incorporates PCA dimensionality reduction and evaluates multiple quantum feature maps (ZFeatureMap, ZZFeatureMap, PauliFeatureMap). Results show Pegasos-QSVC achieving 99.12% recall and 67.41% F1 score, while QNNs reach 54.38% training accuracy. Performance varies across feature maps, with some models exhibiting overfitting.
The proliferation of Internet-connected devices has intensified the risk of large-scale network intrusions, necessitating more advanced detection methodologies for the post quantum era. This work presents, to the best of our knowledge, a comprehensive investigation of quantum machine learning (QML) for anomaly detection on the BoT-IoT dataset, a widely adopted benchmark for IoT security. Leveraging feature-engineered representations, we implement and evaluate four representative quantum classifiers: Quantum Support Vector Classification (QSVC), Quantum Neural Networks (QNN), and Variational Quantum Classifiers (VQC).
This lecture provides an overview of quantum machine learning (QML), focusing on two prominent paradigms: Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs). We begin by introducing QNNs, exploring their structure as variational quantum circuits and the challenges associated with their training. We will cover key practical issues such as the barren plateau phenomenon, which can hinder eective gradient-based optimization. Next, the lecture will turn to Quantum Support Vector Machines, an algorithm that leverages quantum feature maps to encode classical data into a high-dimensional quantum Hilbert space. We will conclude by comparing the strengths and weaknesses of both QNNs and QSVMs in the current QML landscape, oering a realistic perspective on their potential and the research needed to realize their full capabilities.