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.
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.
Abstract Quantum Machine Learning (QML) presents a novel paradigm for financial modeling, particularly for the challenge of few-shot credit risk assessment, which is a critical issue in inclusive finance where data scarcity limits conventional models. To address this, we propose and implement a hybrid quantum-classical workflow. Our methodology first utilizes a classical model ensemble for dimensionality reduction, creating a dense, three-dimensional feature representation from the original data. This representation then serves as input to a Quantum Neural Network (QNN), trained with the parameter-shift rule, which acts as the core classifier. The framework’s efficacy was validated through numerical simulations and on real hardware using the Quafu Cloud Platform’s ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 ± 0.027 in simulations and an impressive AUC of 0.89 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric—crucial for minimizing false negatives in risk control. This study provides a pragmatic blueprint for applying QML to data-constrained financial scenarios and offers valuable empirical evidence of its potential in the NISQ era.
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.
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.
Quantum transfer learning combines pretrained classical deep learning models with quantum circuits to reuse expressive feature representations while limiting the number of trainable parameters. In this work, we introduce a family of compact quantum transfer learning architectures that attach variational quantum classifiers to frozen convolutional backbones for image classification. We instantiate and evaluate several classical-quantum hybrid models implemented in PennyLane and Qiskit, and systematically compare them with a classical transfer-learning baseline across heterogeneous image datasets. To ensure a realistic assessment, we evaluate all approaches under both ideal simulation and noisy emulation using noise models calibrated from IBM quantum hardware specifications, as well as on real IBM quantum hardware. Experimental results show that the proposed quantum transfer learning architectures achieve competitive and, in several cases, superior accuracy while consistently reducing training time and energy consumption relative to the classical baseline. Among the evaluated approaches, PennyLane-based implementations provide the most favorable trade-off between accuracy and computational efficiency, suggesting that hybrid quantum transfer learning can offer practical benefits in realistic NISQ era settings when feature extraction remains classical.
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.
In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a quantum neural network (QNN) based on a single qudit, in which both data features and trainable parameters are co-encoded within a unified unitary evolution generated by the full Lie algebra. This design explores the entire Hilbert space while enabling interpretability through the magnitudes of the learned coefficients. We benchmark our model on a real-world, imbalanced credit-risk dataset from Taiwan. The proposed QNN consistently outperforms LR and reaches the results of random forest models in macro-F1 score while preserving a transparent correspondence between learned parameters and input feature importance. To quantify the interpretability of the proposed model, we introduce two complementary metrics: (i) the edit distance between the model's feature ranking and that of LR, and (ii) a feature-poisoning test where selected features are replaced with noise. Results indicate that the proposed quantum model achieves competitive performance while offering a tractable path toward interpretable quantum learning.