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.
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.
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
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.
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.
Financial datasets possess high dimensionality and non-linear structure that challenge classical models in providing necessary forecastings. This work propose a hybrid Quantum-Enhanced Support Vector Machine (QSVM) that encodes technical indicators via a fixed ZZFeatureMap and computes a quantum kernel utilising a trainable Real Amplitude circuit and a classical SVM. The study adopts rigorous statistical validation with 10 independent runs, stratified with 5-fold CV, mean with 95% confidence intervals and uses McNemar’s test for paired significance. Realistic resource analysis and optional NISQ noise settings are included with qubits, circuit depth, two-qubit gates and shots. On a combined financial dataset, including S&P 500 stocks, cryptocurrencies, forex pairs and FRED economic indicators, QSVM achieves modest but statistically significant gains over tuned classical baselines, with advantages widening at high dimensionality. Results clarify simulation overheads and expected noise-induced degradation, supporting a cautious and reproducible assessment of QSVM for financial forecasting.
Cardiovascular disease (CVD) is the leading cause of global mortality, with over 19.8 million deaths recorded in 2022, underscoring the urgent necessity for precise early-stage computational diagnostic techniques. The traditional machine learning approaches tend to fail in the nonlinear feature interactions of high-dimensional clinical data. This paper provides a Quantum Support Vector Machine (QSVM) framework that uses a fidelity-based quantum kernel, calculated using PennyLane Lightning, and a qubit simulator to map patient data into a high-dimensional Hilbert space, enabling complex feature interactions that are unattainable with traditional kernels. The model was tested on a publicly available UCI-based heart disease dataset that included 1,025 patient records and 13 clinical features, such as chest pain type, ST depression, maximum heart rate, and thalassemia test results, with a near-balanced distribution between the classes (51.3% positive, 48.7% negative). All 13 features were represented in quantum states with a Hadamard-RY feature map applied to 13 qubits, and quantum pipelining was used to perform batch executions of the feature map and reduce simulation overhead. The QSVM achieved 93.90% accuracy, outperforming the classical SVM baseline, which achieved 92.50% on the same dataset. Complexity analysis shows O(d) depth, linear qubit scaling, and NISQ-compatible scalable quantum biomedical diagnostics.
High-Frequency Financial Forecasting is important in algorithmic trading as it helps make decisions based on high-frequency data in milliseconds. This challenge has been found to be unsolvable using conventional machine learning approaches, such as classical Support Vector Machines (SVMs), Random Forest (RF), and deep learning models, which were not able to capture the nonlinear and high-dimensional patterns embedded in the high-frequency datasets, leading to poor prediction performance. This paper proposes a Supervised Quantum Support Vector Machine (QSVM) model, which is tailored for the high-frequency financial forecasting task. The QSVM can effectively encode the market information and enhance the predictability while maintaining the computational efficiency through quantum feature mapping and high-dimensional Hilbert space. This process includes encoding of tick-level market microstructure data, stock and futures prices, and technical indicators into quantum states and subsequent training using a quantum-classical hybrid optimization loop. Classical SVM, Random Forest, LSTM, and hybrid QML are used as the benchmarks for the proposed QSVM using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio. The results obtained from the experiment show that the QSVM has better accuracy (91.2%), RMSE (0.018), and latency (22 ms) as well. Hence, the QSVM shows better performance when compared to other methods and is capable of modeling the markets that have a fast change in dynamics. High-dimensional quantum kernel plays a part in enhancing the ability to separate non-linearities. Therefore, the QSVM could be a potential solution to apply in algorithmic trading. The limitations are hardware constraints of NISQ and restrictions on the size of the dataset. Future directions of research will include the scalability of the model and the use of ensemble approaches.