Customer churn prediction is important in the banking sector for improving customer retention and reducing revenue loss. Traditional Support Vector Machines (SVM) face scalability challenges when processing high-dimensional and non-linear customer data. This study proposes a Quantum Support Vector Machine (QSVM) for churn prediction by combining classical preprocessing with quantum machine learning. The data is cleaned, encoded, scaled, and reduced using Principal Component Analysis (PCA). The resulting features are encoded into quantum states, and a quantum kernel is used for classification. The model is implemented using Python and Qiskit and evaluated on the Qiskit Aer simulator. Performance is assessed using accuracy, precision, recall, and F1-score. The proposed QSVM achieves approximately 80% accuracy, demonstrating strong non-churn prediction and moderate churn identification. The results indicate the potential of quantum machine learning for banking churn prediction. Keywords : Customer Churn Prediction, Quantum Support Vector Machine (QSVM), Quantum Machine Learning, Banking, Principal Component Analysis (PCA), Quantum Kernel, Qiskit, Qiskit Aer, Machine Learning, Customer Retention
Customer churn prediction is important in the banking sector for improving customer retention and reducing revenue loss. Traditional Support Vector Machines (SVM) face scalability challenges when processing high-dimensional and non-linear customer data. This study proposes a Quantum Support Vector Machine (QSVM) for churn prediction by combining classical preprocessing with quantum machine learning. The data is cleaned, encoded, scaled, and reduced using Principal Component Analysis (PCA). The resulting features are encoded into quantum states, and a quantum kernel is used for classification. The model is implemented using Python and Qiskit and evaluated on the Qiskit Aer simulator. Performance is assessed using accuracy, precision, recall, and F1-score. The proposed QSVM achieves approximately 80% accuracy, demonstrating strong non-churn prediction and moderate churn identification. The results indicate the potential of quantum machine learning for banking churn prediction. Keywords : Customer Churn Prediction, Quantum Support Vector Machine (QSVM), Quantum Machine Learning, Banking, Principal Component Analysis (PCA), Quantum Kernel, Qiskit, Qiskit Aer, Machine Learning, Customer Retention
Churn prediction of customers is highly significant in banking as the cost of retention is much lower than the cost of acquisition. Usually individuals operate with the standard ML frameworks, such as SVM, although they have a ceiling when the data is of high dimension and extremely non-linear - they become more and more sluggish and are inadequate to actually model all the complexity. The paper presents a Quantum Support Vector Machine (QSVM) that is used to predict the customers who are going to leave the bank. Unlike The preprocessing in classical code, such as clean up the data, encoding of categories, scaling and then PCA to reduce the feature set to one that fits within the small number of qubits, all proceeds as normal. Then we apply the shrunk data to a quantum state, using ZZFeatureMap, and compute a quantum kernel which informs us of the similarity between data points in Hilbert space. Initialization of the QSVM in Python using Qiskit and then running it on the Qiskit Aer simulator. It is reasonably good: overall accuracy of around 88 percent and good scores on non-churn customer identification and fair adequacy in identifying churners. These figures demonstrate that quantum-enhanced ML might be a viable alternative to quantum hardware in the realm of real business analytics and is going to become even more feasible as quantum technology advances.
SupportVector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. Businesses across industries face challenges in improving customer retention and reducing churn, making predictive models essential for identifying at-risk customers and enhancing revenue. This paper investigates an optimized quantum embedding kernel for SVM (Quantum SVM - QSVM) applied to a public bank customer dataset, featuring variables on customer relationships and churn indicators. While focused on the financial sector, the methodology is broadly applicable for reducing churn and boosting revenue across industries. QSVM performance is compared to SVMs with established kernels, including Radial Basis Function (RBF), linear, polynomial, and sigmoid. Experiments varied the number of variables from two to seven to evaluate their impact on model performance and kernel behavior. The experiments were conducted on quantum simulators, which faced scalability challenges addressed using reduced datasets. Even so, this study sheds light on the potential of QSVMs to effectively manage increasing numbers of variables in predictive models, offering valuable insights into their capability to handle complex, high-dimensional data and their applicability in real-world scenarios.
This paper evaluates the performance of two quantum machine learning algorithms: Quantum Support Vector Machine (QSVM) and Quantum Principal Component Analysis (Q-PCA). The scalability of QSVM is analyzed across increasing dataset sizes and feature dimensions, revealing faster-than-linear growth in kernel evaluation and SVM training times. However, this computational overhead is accompanied by a notable decline in classification accuracy, highlighting challenges in applying QSVM to larger or more complex datasets. In contrast, Q-PCA demonstrates near-linear scalability with dataset size and qubit count, leveraging the independence of quantum circuit execution and structured operations to maintain efficiency. A QNN-based implementation of Q-PCA further validates its ability to align data with principal components, maximize variance, and enable effective dimensionality reduction. These findings emphasize the potential of Q-PCA for efficient quantum-enhanced data analysis while underscoring the need for optimization in QSVM and further exploration of Q-PCA’s capabilities for real-world quantum computing applications.
Cyberbullying has become much more common due to the proliferation of social media, which has devastating effects on users' mental health and relationships. Automatic identification of abusive textual content has therefore become an important research problem. Cyberbullying detection has seen extensive use of classical machine learning approaches, notably Support Vector Machines (SVMs); however, their effectiveness can be limited when handling high-dimensional and context-rich textual features. Recent developments in quantum computing introduce new computational paradigms that may enable more expressive feature representations through quantum-enhanced learning models. In this study, we explore the potential of a hybrid. Nowadays classical-quantum architecture to apply a Quantum Support Vector Machine (QSVM) to detect cyberbullying. Experiments were performed on a publicly available Kaggle dataset containing 35,824 labeled tweets. Before TF-IDF feature extraction, the text data underwent preprocessing via cleaning and tokenization. Dimension reduction was carried out using Principal Component Analysis (PCA) in respect of qubit constraints. Then, they were embedded into quantum states using angle embedding. The accuracy, precision, recall, and F1-score measures were used to compare QSVM models with various qubit configurations (2, 4, and 6), as well as a traditional SVM baseline, under an 80-20 stratified train-test split. The findings indicate that the quality of QSVM performance is highest with the increase in qubit numbers and the maximum accuracy of the quantum computing method is 60 percent according to six qubits, whereas the classical SVM achieves 67.5 percent. Although the quantum model currently incurs higher computational overhead due to simulator-based implementation, increasing qubit dimensionality enhances its ability to capture complex and non-linear linguistic patterns. These observations indicate that QSVM-based cyberbullying detection remains exploratory at present, with scope for further improvement as quantum hardware and algorithms advance.
With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.
This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques. In the method, clinical data of HD are preprocessed, and then features are optimized using principal component analysis (PCA). After that, the resulting features are encoded into quantum states with five different QFM methods, namely angle encoding (AE), amplitude encoding (AmE), basis encoding (BE), Pauli encoding (PE), and ZZ feature map (ZZFM). Finally, four quantum classifiers, such as quantum support vector machine (QSVM), quantum k-nearest neighbor (QKNN), quantum random forest (QRF), and variational quantum circuit (VQC), are evaluated to predict the HD from the encoded states. Experimental results show that QSVM with AE achieved the best performance, with an overall accuracy of 90.26%, specificity of 83.42%, sensitivity of 92.16%, precision of 88.89%, F1-score of 89.68%, and kappa value of 0.7608. These results are superior to those from classical state-of-the-art methods. This research finding suggests QML methods can capture complex nonlinear relationships in clinical data effectively and thus improve diagnostic reliability.