Quantum Support Vector Classifier (QSVC) is a quantum machine learning model that solves classification problems. QSVC leverages quantum advantage by utilizing a quantum kernel. A quantum kernel requires a Pauli Feature Map (a data encoding circuit) to encode the classical data into the quantum state space. A Pauli Feature Map's performance can be impacted by two of its key attributes-repetitions and entanglement. In this work, we study the performance of Pauli Feature Map-in terms of QSVC execution time and accuracy-for different combinations of repetitions and entanglement on two different datasets.
Quantum Support Vector Classifier (QSVC) is a quantum machine learning model that solves classification problems. QSVC leverages quantum advantage by utilizing a quantum kernel. A quantum kernel requires a Pauli Feature Map (a data encoding circuit) to encode the classical data into the quantum state space. A Pauli Feature Map's performance can be impacted by two of its key attributes-repetitions and entanglement. In this work, we study the performance of Pauli Feature Map-in terms of QSVC execution time and accuracy-for different combinations of repetitions and entanglement on two different datasets.
A quantum feature map encodes classical data to the quantum state space by using a quantum circuit. The repetition of such a circuit during encoding is a customize value known as depth. Encoding data to quantum state is a must step for applying Quantum machine learning (QML) to classical data. Utilizing different feature map techniques by varying several depths, this research uses a kernel-based quantum support vector machine (QSVM) to classify several datasets. The fundamental aim of such activities is to check whether feature map techniques can make any sense to supervised QML concerning their depths and the outcomes analysis concludes that maximum accuracy of any supervised QML model is obtained due to the selection of an essential feature map approach with appropriate circuit depth. The results also present that time consumption of any feature map technique increases linearly with the increase of feature map circuit depth. However, the outcome of this research will help anyone to estimate the feature map technique and circuit depth when executing QML.
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.
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
This work presents a quantum machine learning approach for bipartite entanglement detection in 3×3 quantum systems using a support vector machine with quantum kernels. The classifier is trained to distinguish separable from entangled states, which includes both PPT-detectable and PPT-non-detectable instances, based on classical simulations of parameterized quantum feature maps combined with dimensionality reduction via PCA. Our results show that Pauli-based feature map kernels achieve higher classification accuracy than amplitude embedding at the same PCA dimension, reaching up to 78% accuracy for PCA =8, while amplitude embedding requires PCA =32 to attain comparable performance. We also show that increasing the number of Pauli feature map repetitions does not lead to consistent accuracy improvements, despite significantly increasing circuit depth and computational overhead.
Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.