This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.
In modern financial information systems, the growing complexity of credit risk assessment for small and medium-sized enterprises (SMEs) creates increasing demands for highly accurate, scalable, and computationally efficient predictive models.Traditional credit scoring approaches based on classical statistical methods and conventional neural networks often demonstrate limited performance when processing nonlinear financial dependencies, noisy datasets, and highdimensional feature spaces.These limitations become especially significant under restricted training conditions, where the amount of labeled financial data is limited and rapid model convergence is required for practical deployment in banking and risk management environments.In such conditions, hybrid quantum-classical machine learning has emerged as a promising direction that combines the representational capabilities of classical neural networks with the computational potential of quantum variational circuits.This paper proposes a hybrid quantum-classical neural network architecture designed for SME credit risk assessment under limited training conditions.The proposed model integrates classical dense layers with a variational quantum circuit that performs nonlinear feature transformation through quantum embedding, parameterized quantum gates, and entanglement operations.Particular attention is devoted to efficient data encoding and optimization of variational circuit depth in order to reduce training complexity while preserving predictive stability.
Quantum machine learning has been shown to offer exponential computational boosts compared to current classical models through the exploitation of quantum mechanical properties such as entanglement and superposition. The finance sub field of fin-tech, where financial services are constructed around a customer's demands, is believed to benefit with the use of hybrid quantum-classical machine learning algorithms. Classical algorithms struggle to create accurate, complex, and prompt financial analytical models resulting in over 25% of small and medium sized financial institutions losing customers each year. This study explores whether a hybrid quantum neural network (QCNN) design provides a quantum advantage in either accuracy or time in the fin tech task of credit defaulting. The hybrid quantum neural network model uses a variational quantum circuit built on a continuous variable architecture. The data flow of the network consists of a classical network, quantum data encoding (which uses quantum squeezers, interferometers, displacement, and Kerr gates), QCNN, and measurement. Although the quantum model took a longer time to train, it reported a higher prediction accuracy of 97.63% compared to 91.20% for the classical network. This work demonstrates the future implications of quantum integration in the financial field and the quantum advantage it offers on classification tasks.
Credit card fraud detection remains a challenging binary classification problem because fraudulent transactions are rare, transaction patterns are complex, and false negatives may have important operational consequences. This study presents a Hybrid Classical–Quantum-Inspired Neural Network (HCQNN) with a simulated variational quantum circuit for credit card fraud detection. The proposed framework combines classical preprocessing, SMOTE-based class balancing, neural network-based feature learning, and quantum-inspired variational feature transformation. The model was evaluated using the Credit Card Fraud Detection dataset after applying SMOTE to the training data and was compared with three classical baseline classifiers: Logistic Regression, Decision Tree, and Linear Support Vector Machine. The experimental results show that the proposed HCQNN achieved an AUC of 97.85%, precision of 91.20%, recall of 90.10%, and an F1-score of 90.64%. These values indicate improved classification balance, particularly in the detection of minority-class fraud cases, compared with the selected baseline models. Training and validation behaviour also showed stable convergence, with training and validation accuracies exceeding 96% and 95%, respectively. Since the variational quantum circuit was simulated on classical hardware, the findings should be interpreted as evidence of the value of hybrid feature learning and quantum-inspired transformations rather than as proof of quantum computational advantage. The study provides a basis for further evaluation using broader datasets, additional baseline models, and real quantum hardware.
We propose an efficient application of the hybrid quantum-classical transfer learning model to improve the performance of credit risk classification. The Quantum Transfer Learning (QTL) model presented in this study consists of a frozen classical feature extractor and a quantum refinement layer which is trained on the features extracted from the real-world credit risk dataset. For baseline comparison, we evaluated a standalone Quantum Neural Network (QNN) that was trained directly on the credit risk dataset. Our experimental evaluations on a real-world normalized credit risk dataset reveal that the proposed QTL model significantly outperforms the standalone QNN and achieves results on par with the classical neural network.
This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achievinga perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterizedgate selection, and the integration of the quantum layer with classical neural network components.This work contributes to the growing body of research on hybrid quantum-classical models and theirapplicability to real-world datasets.
Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.